Automatic configuration discovery based on traffic flow data

Description

TECHNICAL FIELD

The present technology pertains to network traffic flow stitching and flow stitching network traffic flow segments at a middlebox in a network environment.

BACKGROUND

Currently, sensors deployed in a network can be used to gather network traffic data related to nodes operating in the network. The network traffic data can include metadata relating to a packet, a collection of packets, a flow, a bidirectional flow, a group of flows, a session, or a network communication of another granularity. That is, the network traffic data can generally include any information describing communication on all layers of the Open Systems Interconnection (OSI) model. For example, the network traffic data can include source/destination MAC address, source/destination IP address, protocol, port number, etc. In some embodiments, the network traffic data can also include summaries of network activity or other network statistics such as number of packets, number of bytes, number of flows, bandwidth usage, response time, latency, packet loss, jitter, and other network statistics.

Gathered network traffic data can be analyzed to provide insights into the operation of the nodes in the network, otherwise referred to as analytics. In particular, discovered application or inventories, application dependencies, policies, efficiencies, resource and bandwidth usage, and network flows can be determined for the network using the network traffic data.

Sensors deployed in a network can be used to gather network traffic data on a client and server level of granularity. For example, network traffic data can be gathered for determining which clients are communicating which servers and vice versa. However, sensors are not currently deployed or integrated with systems to gather network traffic data for different segments of traffic flows forming the traffic flows between a server and a client. Specifically, current sensors gather network traffic data as traffic flows directly between a client and a server while ignoring which nodes, e.g. middleboxes, the traffic flows actually pass through in passing between a server and a client. This effectively treats the network environment between servers and clients as a black box and leads to gaps in network traffic data and traffic flows indicated by the network traffic data.

In turn, such gaps in network traffic data and corresponding traffic flows can lead to deficiencies in diagnosing problems within a network environment. For example, a problem stemming from an incorrectly configured middlebox might be diagnosed as occurring at a client as the flow between the client and a server is treated as a black box. In another example, gaps in network traffic data between a server and a client can lead to an inability to determine whether policies are correctly enforced at a middlebox between the server and the client. There therefore exist needs for systems, methods, and computer-readable media for generating network traffic data at nodes between servers and clients, e.g. at middleboxes between the servers and clients. In particular, there exist needs for systems, methods, and computer-readable media for stitching together traffic flows at nodes between servers and clients to generate a more complete and detailed traffic flow, e.g. between the servers and the clients.

Further, as nodes between a client and a server, e.g. middleboxes, are treated as black boxes it is difficult to identify how the nodes are actually configured to operate in a network environment. In particular, as nodes are treated as black boxes with respect to traffic flows through the nodes, it is difficult to automatically identify how the nodes are actually configured to operate in a network environment. This is problematic as a node can be configured to operate according to an expected configuration but actually be operating at a different or unexpected configuration. There, therefore exist needs for systems, methods, and computer-readable media for discovering configurations of nodes in a network environment between clients and servers based on traffic flows through the nodes.

Additionally, network environments typically include a large number of intermediate nodes, e.g. middleboxes, between clients and servers. Typically, identifying a configuration of these intermediate nodes requires effort and intervention from network administrators. For example, network administrators have to gather data of servers a node is communicating with and analyze the data, e.g. using application discovery, to determine configurations of the nodes. This can be extremely burdensome for typical network environments that include large numbers of nodes between clients and servers. There, therefore exist needs for systems, methods, and computer-readable media for automatically discovering configurations of nodes in a network environment between clients and servers.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to describe the manner in which the above-recited and other advantages and features of the disclosure can be obtained, a more particular description of the principles briefly described above will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings. Understanding that these drawings depict only exemplary embodiments of the disclosure and are not therefore to be considered to be limiting of its scope, the principles herein are described and explained with additional specificity and detail through the use of the accompanying drawings in which:

FIG. 1 illustrates an example network traffic monitoring system;

FIG. 2 illustrates an example of a network environment;

FIG. 3 depicts a diagram of an example network environment for stitching together traffic flow segments across multiple nodes between a client and a server;

FIG. 4 illustrates a flowchart for an example method of forming a cross-middlebox stitched traffic flow across middleboxes;

FIG. 5 shows an example middlebox traffic flow stitching system;

FIG. 6 depicts a diagram of an example network environment for discovering configurations of middleboxes based on stitched traffic flows about the middleboxes;

FIG. 7 illustrates a flowchart for an example method of automatically identifying a configuration of a middlebox using stitched traffic flow segments at the middlebox;

FIG. 8 illustrates an example network device in accordance with various embodiments; and

FIG. 9 illustrates an example computing device in accordance with various embodiments.

DESCRIPTION OF EXAMPLE EMBODIMENTS

Various embodiments of the disclosure are discussed in detail below. While specific implementations are discussed, it should be understood that this is done for illustration purposes only. A person skilled in the relevant art will recognize that other components and configurations can be used without parting from the spirit and scope of the disclosure. Thus, the following description and drawings are illustrative and are not to be construed as limiting. Numerous specific details are described to provide a thorough understanding of the disclosure. However, in certain instances, well-known or conventional details are not described in order to avoid obscuring the description. References to one or an embodiment in the present disclosure can be references to the same embodiment or any embodiment; and, such references mean at least one of the embodiments.

Reference to “one embodiment” or “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the disclosure. The appearances of the phrase “in one embodiment” in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Moreover, various features are described which can be exhibited by some embodiments and not by others.

The terms used in this specification generally have their ordinary meanings in the art, within the context of the disclosure, and in the specific context where each term is used. Alternative language and synonyms can be used for any one or more of the terms discussed herein, and no special significance should be placed upon whether or not a term is elaborated or discussed herein. In some cases, synonyms for certain terms are provided. A recital of one or more synonyms does not exclude the use of other synonyms. The use of examples anywhere in this specification including examples of any terms discussed herein is illustrative only, and is not intended to further limit the scope and meaning of the disclosure or of any example term. Likewise, the disclosure is not limited to various embodiments given in this specification.

Without intent to limit the scope of the disclosure, examples of instruments, apparatus, methods and their related results according to the embodiments of the present disclosure are given below. Note that titles or subtitles can be used in the examples for convenience of a reader, which in no way should limit the scope of the disclosure. Unless otherwise defined, technical and scientific terms used herein have the meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains. In the case of conflict, the present document, including definitions will control.

Additional features and advantages of the disclosure will be set forth in the description which follows, and in part will be obvious from the description, or can be learned by practice of the herein disclosed principles. The features and advantages of the disclosure can be realized and obtained by means of the instruments and combinations particularly pointed out in the appended claims. These and other features of the disclosure will become more fully apparent from the following description and appended claims, or can be learned by the practice of the principles set forth herein.

OVERVIEW

A method can include collecting flow records of traffic flow segments at a middlebox in a network environment. The flow records can include transaction identifiers assigned to the traffic flow segments. Sources and destinations of the traffic flow segments in the network environment with respect to the middlebox can be identified using the flow records. The method can also include stitching together the traffic flow segments to form a plurality of stitched traffic flows at the middlebox in the network environment. The traffic flow segments can be stitched together based on the transaction identifiers assigned to the traffic flow segments and the sources and destinations of the traffic flow segments in the network environment with respect to the middlebox. Further, the method can include automatically identifying a configuration of the middlebox operating in the network environment based on the stitched traffic flows at the middlebox in the network environment.

A system can collect flow records of traffic flow segments at a middlebox in a network environment. The flow records can include transaction identifiers assigned to the traffic flow segments. Sources and destinations of the traffic flow segments in the network environment with respect to the middlebox can be identified using the flow records. The system can also stitch together the traffic flow segments to form a plurality of stitched traffic flows at the middlebox in the network environment. The stitched traffic flows can pass through the middlebox ultimately between one or more clients and one or more servers and the plurality of stitched traffic flows can include client-server stitched traffic flows that extend between the one or more clients and the one or more servers. The traffic flow segments can be stitched together based on the transaction identifiers assigned to the traffic flow segments and the sources and destinations of the traffic flow segments in the network environment with respect to the middlebox. Further, the system can automatically identify a configuration of the middlebox operating in the network environment to facilitate network service access to the one or more clients through the one or more servers based on the stitched traffic flows at the middlebox in the network environment.

A system can collect flow records of traffic flow segments at a middlebox in a network environment. Sources and destinations of the traffic flow segments in the network environment with respect to the middlebox can be identified using the flow records. The system can also stitch together the traffic flow segments to form a plurality of stitched traffic flows at the middlebox in the network environment. The traffic flow segments can be stitched together based on the sources and destinations of the traffic flow segments in the network environment with respect to the middlebox. Further, the system can automatically identify a configuration of the middlebox operating in the network environment based on the stitched traffic flows at the middlebox in the network environment.

EXAMPLE EMBODIMENTS

The disclosed technology addresses the need in the art for monitoring network environments, e.g. to diagnose and prevent problems in the network environment. The present technology involves system, methods, and computer-readable media for automatically identifying configurations of nodes between clients and servers in a network environment using stitched traffic flows through the nodes, e.g. for diagnosing and preventing problems in a network environment.

The present technology will be described in the following disclosure as follows. The discussion begins with an introductory discussion of network traffic data collection and a description of an example network traffic monitoring system and an example network environment, as shown in FIGS. 1 and 2. A discussion of example systems and methods for stitching together network traffic flows at nodes, as shown in FIGS. 3-5, will then follow. A discussion of systems and methods for automatically discovering a configuration of nodes using stitched traffic flows at the nodes, as shown in FIGS. 6 and 7, will then follow. The disclosure concludes with a discussion of example network devices and computing devices, as illustrated in FIGS. 6 and 7. The disclosure now turns to an introductory discussion of network sensor data collection based on network traffic flows and clustering of nodes in a network for purposes of collecting data based on network traffic flows.

Sensors implemented in networks are traditionally limited to collecting packet data at networking devices. In some embodiments, networks can be configured with sensors at multiple points, including on networking devices (e.g., switches, routers, gateways, firewalls, deep packet inspectors, traffic monitors, load balancers, etc.), physical servers, hypervisors or shared kernels, virtual partitions (e.g., VMs or containers), and other network elements. This can provide a more comprehensive view of the network. Further, network traffic data (e.g., flows) can be associated with, or otherwise include, host and/or endpoint data (e.g., host/endpoint name, operating system, CPU usage, network usage, disk space, logged users, scheduled jobs, open files, information regarding files stored on a host/endpoint, etc.), process data (e.g., process name, ID, parent process ID, path, CPU utilization, memory utilization, etc.), user data (e.g., user name, ID, login time, etc.), and other collectible data to provide more insight into network activity.

Sensors implemented in a network at multiple points can be used to collect data for nodes grouped together into a cluster. Nodes can be clustered together, or otherwise a cluster of nodes can be identified using one or a combination of applicable network operation factors. For example, endpoints performing similar workloads, communicating with a similar set of endpoints or networking devices, having similar network and security limitations (i.e., policies), and sharing other attributes can be clustered together.

In some embodiments, a cluster can be determined based on early fusion in which feature vectors of each node comprise the union of individual feature vectors across multiple domains. For example, a feature vector can include a packet header-based feature (e.g., destination network address for a flow, port, etc.) concatenated to an aggregate flow-based feature (e.g., the number of packets in the flow, the number of bytes in the flow, etc.). A cluster can then be defined as a set of nodes whose respective concatenated feature vectors are determined to exceed specified similarity thresholds (or fall below specified distance thresholds).

In some embodiments, a cluster can be defined based on late fusion in which each node can be represented as multiple feature vectors of different data types or domains. In such systems, a cluster can be a set of nodes whose similarity (and/or distance measures) across different domains, satisfy specified similarity (and/or distance) conditions for each domain. For example, a first node can be defined by a first network information-based feature vector and a first process-based feature vector while a second node can be defined by a second network information-based feature vector and a second process-based feature vector. The nodes can be determined to form a cluster if their corresponding network-based feature vectors are similar to a specified degree and their corresponding process-based feature vectors are only a specified distance apart.

Referring now to the drawings, FIG. 1 is an illustration of a network traffic monitoring system 100 in accordance with an embodiment. The network traffic monitoring system 100 can include a configuration manager 102, sensors 104, a collector module 106, a data mover module 108, an analytics engine 110, and a presentation module 112. In FIG. 1, the analytics engine 110 is also shown in communication with out-of-band data sources 114, third party data sources 116, and a network controller 118.

The configuration manager 102 can be used to provision and maintain the sensors 104, including installing sensor software or firmware in various nodes of a network, configuring the sensors 104, updating the sensor software or firmware, among other sensor management tasks. For example, the sensors 104 can be implemented as virtual partition images (e.g., virtual machine (VM) images or container images), and the configuration manager 102 can distribute the images to host machines. In general, a virtual partition can be an instance of a VM, container, sandbox, or other isolated software environment. The software environment can include an operating system and application software. For software running within a virtual partition, the virtual partition can appear to be, for example, one of many servers or one of many operating systems executed on a single physical server. The configuration manager 102 can instantiate a new virtual partition or migrate an existing partition to a different physical server. The configuration manager 102 can also be used to configure the new or migrated sensor.

The configuration manager 102 can monitor the health of the sensors 104. For example, the configuration manager 102 can request for status updates and/or receive heartbeat messages, initiate performance tests, generate health checks, and perform other health monitoring tasks. In some embodiments, the configuration manager 102 can also authenticate the sensors 104. For instance, the sensors 104 can be assigned a unique identifier, such as by using a one-way hash function of a sensor's basic input/out system (BIOS) universally unique identifier (UUID) and a secret key stored by the configuration image manager 102. The UUID can be a large number that can be difficult for a malicious sensor or other device or component to guess. In some embodiments, the configuration manager 102 can keep the sensors 104 up to date by installing the latest versions of sensor software and/or applying patches. The configuration manager 102 can obtain these updates automatically from a local source or the Internet.

The sensors 104 can reside on various nodes of a network, such as a virtual partition (e.g., VM or container) 120; a hypervisor or shared kernel managing one or more virtual partitions and/or physical servers 122, an application-specific integrated circuit (ASIC) 124 of a switch, router, gateway, or other networking device, or a packet capture (pcap) 126 appliance (e.g., a standalone packet monitor, a device connected to a network devices monitoring port, a device connected in series along a main trunk of a datacenter, or similar device), or other element of a network. The sensors 104 can monitor network traffic between nodes, and send network traffic data and corresponding data (e.g., host data, process data, user data, etc.) to the collectors 106 for storage. For example, the sensors 104 can sniff packets being sent over its hosts' physical or virtual network interface card (NIC), or individual processes can be configured to report network traffic and corresponding data to the sensors 104. Incorporating the sensors 104 on multiple nodes and within multiple partitions of some nodes of the network can provide for robust capture of network traffic and corresponding data from each hop of data transmission. In some embodiments, each node of the network (e.g., VM, container, or other virtual partition 120, hypervisor, shared kernel, or physical server 122, ASIC 124, pcap 126, etc.) includes a respective sensor 104. However, it should be understood that various software and hardware configurations can be used to implement the sensor network 104.

As the sensors 104 capture communications and corresponding data, they can continuously send network traffic data to the collectors 106. The network traffic data can include metadata relating to a packet, a collection of packets, a flow, a bidirectional flow, a group of flows, a session, or a network communication of another granularity. That is, the network traffic data can generally include any information describing communication on all layers of the Open Systems Interconnection (OSI) model. For example, the network traffic data can include source/destination MAC address, source/destination IP address, protocol, port number, etc. In some embodiments, the network traffic data can also include summaries of network activity or other network statistics such as number of packets, number of bytes, number of flows, bandwidth usage, response time, latency, packet loss, jitter, and other network statistics.

The sensors 104 can also determine additional data, included as part of gathered network traffic data, for each session, bidirectional flow, flow, packet, or other more granular or less granular network communication. The additional data can include host and/or endpoint information, virtual partition information, sensor information, process information, user information, tenant information, application information, network topology, application dependency mapping, cluster information, or other information corresponding to each flow.

In some embodiments, the sensors 104 can perform some preprocessing of the network traffic and corresponding data before sending the data to the collectors 106. For example, the sensors 104 can remove extraneous or duplicative data or they can create summaries of the data (e.g., latency, number of packets per flow, number of bytes per flow, number of flows, etc.). In some embodiments, the sensors 104 can be configured to only capture certain types of network information and disregard the rest. In some embodiments, the sensors 104 can be configured to capture only a representative sample of packets (e.g., every 1,000th packet or other suitable sample rate) and corresponding data.

Since the sensors 104 can be located throughout the network, network traffic and corresponding data can be collected from multiple vantage points or multiple perspectives in the network to provide a more comprehensive view of network behavior. The capture of network traffic and corresponding data from multiple perspectives rather than just at a single sensor located in the data path or in communication with a component in the data path, allows the data to be correlated from the various data sources, which can be used as additional data points by the analytics engine 110. Further, collecting network traffic and corresponding data from multiple points of view ensures more accurate data is captured. For example, a conventional sensor network can be limited to sensors running on external-facing network devices (e.g., routers, switches, network appliances, etc.) such that east-west traffic, including VM-to-VM or container-to-container traffic on a same host, may not be monitored. In addition, packets that are dropped before traversing a network device or packets containing errors cannot be accurately monitored by the conventional sensor network. The sensor network 104 of various embodiments substantially mitigates or eliminates these issues altogether by locating sensors at multiple points of potential failure. Moreover, the network traffic monitoring system 100 can verify multiple instances of data for a flow (e.g., source endpoint flow data, network device flow data, and endpoint flow data) against one another.

In some embodiments, the network traffic monitoring system 100 can assess a degree of accuracy of flow data sets from multiple sensors and utilize a flow data set from a single sensor determined to be the most accurate and/or complete. The degree of accuracy can be based on factors such as network topology (e.g., a sensor closer to the source can be more likely to be more accurate than a sensor closer to the destination), a state of a sensor or a node hosting the sensor (e.g., a compromised sensor/node can have less accurate flow data than an uncompromised sensor/node), or flow data volume (e.g., a sensor capturing a greater number of packets for a flow can be more accurate than a sensor capturing a smaller number of packets).

In some embodiments, the network traffic monitoring system 100 can assemble the most accurate flow data set and corresponding data from multiple sensors. For instance, a first sensor along a data path can capture data for a first packet of a flow but can be missing data for a second packet of the flow while the situation is reversed for a second sensor along the data path. The network traffic monitoring system 100 can assemble data for the flow from the first packet captured by the first sensor and the second packet captured by the second sensor.

As discussed, the sensors 104 can send network traffic and corresponding data to the collectors 106. In some embodiments, each sensor can be assigned to a primary collector and a secondary collector as part of a high availability scheme. If the primary collector fails or communications between the sensor and the primary collector are not otherwise possible, a sensor can send its network traffic and corresponding data to the secondary collector. In other embodiments, the sensors 104 are not assigned specific collectors but the network traffic monitoring system 100 can determine an optimal collector for receiving the network traffic and corresponding data through a discovery process. In such embodiments, a sensor can change where it sends it network traffic and corresponding data if its environments changes, such as if a default collector fails or if the sensor is migrated to a new location and it would be optimal for the sensor to send its data to a different collector. For example, it can be preferable for the sensor to send its network traffic and corresponding data on a particular path and/or to a particular collector based on latency, shortest path, monetary cost (e.g., using private resources versus a public resources provided by a public cloud provider), error rate, or some combination of these factors. In other embodiments, a sensor can send different types of network traffic and corresponding data to different collectors. For example, the sensor can send first network traffic and corresponding data related to one type of process to one collector and second network traffic and corresponding data related to another type of process to another collector.

The collectors 106 can be any type of storage medium that can serve as a repository for the network traffic and corresponding data captured by the sensors 104. In some embodiments, data storage for the collectors 106 is located in an in-memory database, such as dashDB from IBM®, although it should be appreciated that the data storage for the collectors 106 can be any software and/or hardware capable of providing rapid random access speeds typically used for analytics software. In various embodiments, the collectors 106 can utilize solid state drives, disk drives, magnetic tape drives, or a combination of the foregoing according to cost, responsiveness, and size requirements. Further, the collectors 106 can utilize various database structures such as a normalized relational database or a NoSQL database, among others.

In some embodiments, the collectors 106 can only serve as network storage for the network traffic monitoring system 100. In such embodiments, the network traffic monitoring system 100 can include a data mover module 108 for retrieving data from the collectors 106 and making the data available to network clients, such as the components of the analytics engine 110. In effect, the data mover module 108 can serve as a gateway for presenting network-attached storage to the network clients. In other embodiments, the collectors 106 can perform additional functions, such as organizing, summarizing, and preprocessing data. For example, the collectors 106 can tabulate how often packets of certain sizes or types are transmitted from different nodes of the network. The collectors 106 can also characterize the traffic flows going to and from various nodes. In some embodiments, the collectors 106 can match packets based on sequence numbers, thus identifying traffic flows and connection links. As it can be inefficient to retain all data indefinitely in certain circumstances, in some embodiments, the collectors 106 can periodically replace detailed network traffic data with consolidated summaries. In this manner, the collectors 106 can retain a complete dataset describing one period (e.g., the past minute or other suitable period of time), with a smaller dataset of another period (e.g., the previous 2-10 minutes or other suitable period of time), and progressively consolidate network traffic and corresponding data of other periods of time (e.g., day, week, month, year, etc.). In some embodiments, network traffic and corresponding data for a set of flows identified as normal or routine can be winnowed at an earlier period of time while a more complete data set can be retained for a lengthier period of time for another set of flows identified as anomalous or as an attack.

The analytics engine 110 can generate analytics using data collected by the sensors 104. Analytics generated by the analytics engine 110 can include applicable analytics of nodes or a cluster of nodes operating in a network. For example, analytics generated by the analytics engine 110 can include one or a combination of information related to flows of data through nodes, detected attacks on a network or nodes of a network, applications at nodes or distributed across the nodes, application dependency mappings for applications at nodes, policies implemented at nodes, and actual policies enforced at nodes.

Computer networks can be exposed to a variety of different attacks that expose vulnerabilities of computer systems in order to compromise their security. Some network traffic can be associated with malicious programs or devices. The analytics engine 110 can be provided with examples of network states corresponding to an attack and network states corresponding to normal operation. The analytics engine 110 can then analyze network traffic and corresponding data to recognize when the network is under attack. In some embodiments, the network can operate within a trusted environment for a period of time so that the analytics engine 110 can establish a baseline of normal operation. Since malware is constantly evolving and changing, machine learning can be used to dynamically update models for identifying malicious traffic patterns.

In some embodiments, the analytics engine 110 can be used to identify observations which differ from other examples in a dataset. For example, if a training set of example data with known outlier labels exists, supervised anomaly detection techniques can be used. Supervised anomaly detection techniques utilize data sets that have been labeled as normal and abnormal and train a classifier. In a case in which it is unknown whether examples in the training data are outliers, unsupervised anomaly techniques can be used. Unsupervised anomaly detection techniques can be used to detect anomalies in an unlabeled test data set under the assumption that the majority of instances in the data set are normal by looking for instances that seem to fit to the remainder of the data set.

The analytics engine 110 can include a data lake 130, an application dependency mapping (ADM) module 140, and elastic processing engines 150. The data lake 130 is a large-scale storage repository that provides massive storage for various types of data, enormous processing power, and the ability to handle nearly limitless concurrent tasks or jobs. In some embodiments, the data lake 130 is implemented using the Hadoop® Distributed File System (HDFS™) from Apache® Software Foundation of Forest Hill, Md. HDFS™ is a highly scalable and distributed file system that can scale to thousands of cluster nodes, millions of files, and petabytes of data. HDFS™ is optimized for batch processing where data locations are exposed to allow computations to take place where the data resides. HDFS™ provides a single namespace for an entire cluster to allow for data coherency in a write-once, read-many access model. That is, clients can only append to existing files in the node. In HDFS™, files are separated into blocks, which are typically 64 MB in size and are replicated in multiple data nodes. Clients access data directly from data nodes.

In some embodiments, the data mover 108 receives raw network traffic and corresponding data from the collectors 106 and distributes or pushes the data to the data lake 130. The data lake 130 can also receive and store out-of-band data 114, such as statuses on power levels, network availability, server performance, temperature conditions, cage door positions, and other data from internal sources, and third party data 116, such as security reports (e.g., provided by Cisco® Systems, Inc. of San Jose, Calif., Arbor Networks® of Burlington, Mass., Symantec® Corp. of Sunnyvale, Calif., Sophos® Group plc of Abingdon, England, Microsoft® Corp. of Seattle, Wash., Verizon® Communications, Inc. of New York, N.Y., among others), geolocation data, IP watch lists, Whois data, configuration management database (CMDB) or configuration management system (CMS) as a service, and other data from external sources. In other embodiments, the data lake 130 can instead fetch or pull raw traffic and corresponding data from the collectors 106 and relevant data from the out-of-band data sources 114 and the third party data sources 116. In yet other embodiments, the functionality of the collectors 106, the data mover 108, the out-of-band data sources 114, the third party data sources 116, and the data lake 130 can be combined. Various combinations and configurations are possible as would be known to one of ordinary skill in the art.

Each component of the data lake 130 can perform certain processing of the raw network traffic data and/or other data (e.g., host data, process data, user data, out-of-band data or third party data) to transform the raw data to a form useable by the elastic processing engines 150. In some embodiments, the data lake 130 can include repositories for flow attributes 132, host and/or endpoint attributes 134, process attributes 136, and policy attributes 138. In some embodiments, the data lake 130 can also include repositories for VM or container attributes, application attributes, tenant attributes, network topology, application dependency maps, cluster attributes, etc.

The flow attributes 132 relate to information about flows traversing the network. A flow is generally one or more packets sharing certain attributes that are sent within a network within a specified period of time. The flow attributes 132 can include packet header fields such as a source address (e.g., Internet Protocol (IP) address, Media Access Control (MAC) address, Domain Name System (DNS) name, or other network address), source port, destination address, destination port, protocol type, class of service, among other fields. The source address can correspond to a first endpoint (e.g., network device, physical server, virtual partition, etc.) of the network, and the destination address can correspond to a second endpoint, a multicast group, or a broadcast domain. The flow attributes 132 can also include aggregate packet data such as flow start time, flow end time, number of packets for a flow, number of bytes for a flow, the union of TCP flags for a flow, among other flow data.

The host and/or endpoint attributes 134 describe host and/or endpoint data for each flow, and can include host and/or endpoint name, network address, operating system, CPU usage, network usage, disk space, ports, logged users, scheduled jobs, open files, and information regarding files and/or directories stored on a host and/or endpoint (e.g., presence, absence, or modifications of log files, configuration files, device special files, or protected electronic information). As discussed, in some embodiments, the host and/or endpoints attributes 134 can also include the out-of-band data 114 regarding hosts such as power level, temperature, and physical location (e.g., room, row, rack, cage door position, etc.) or the third party data 116 such as whether a host and/or endpoint is on an IP watch list or otherwise associated with a security threat, Whois data, or geocoordinates. In some embodiments, the out-of-band data 114 and the third party data 116 can be associated by process, user, flow, or other more granular or less granular network element or network communication.

The process attributes 136 relate to process data corresponding to each flow, and can include process name (e.g., bash, httpd, netstat, etc.), ID, parent process ID, path (e.g., /usr2/username/bin/, /usr/local/bin, /usr/bin, etc.), CPU utilization, memory utilization, memory address, scheduling information, nice value, flags, priority, status, start time, terminal type, CPU time taken by the process, the command that started the process, and information regarding a process owner (e.g., user name, ID, user's real name, e-mail address, user's groups, terminal information, login time, expiration date of login, idle time, and information regarding files and/or directories of the user).

The policy attributes 138 contain information relating to network policies. Policies establish whether a particular flow is allowed or denied by the network as well as a specific route by which a packet traverses the network. Policies can also be used to mark packets so that certain kinds of traffic receive differentiated service when used in combination with queuing techniques such as those based on priority, fairness, weighted fairness, token bucket, random early detection, round robin, among others. The policy attributes 138 can include policy statistics such as a number of times a policy was enforced or a number of times a policy was not enforced. The policy attributes 138 can also include associations with network traffic data. For example, flows found to be non-conformant can be linked or tagged with corresponding policies to assist in the investigation of non-conformance.

The analytics engine 110 can include any number of engines 150, including for example, a flow engine 152 for identifying flows (e.g., flow engine 152) or an attacks engine 154 for identify attacks to the network. In some embodiments, the analytics engine can include a separate distributed denial of service (DDoS) attack engine 155 for specifically detecting DDoS attacks. In other embodiments, a DDoS attack engine can be a component or a sub-engine of a general attacks engine. In some embodiments, the attacks engine 154 and/or the DDoS engine 155 can use machine learning techniques to identify security threats to a network. For example, the attacks engine 154 and/or the DDoS engine 155 can be provided with examples of network states corresponding to an attack and network states corresponding to normal operation. The attacks engine 154 and/or the DDoS engine 155 can then analyze network traffic data to recognize when the network is under attack. In some embodiments, the network can operate within a trusted environment for a time to establish a baseline for normal network operation for the attacks engine 154 and/or the DDoS.

The analytics engine 110 can further include a search engine 156. The search engine 156 can be configured, for example to perform a structured search, an NLP (Natural Language Processing) search, or a visual search. Data can be provided to the engines from one or more processing components.

The analytics engine 110 can also include a policy engine 158 that manages network policy, including creating and/or importing policies, monitoring policy conformance and non-conformance, enforcing policy, simulating changes to policy or network elements affecting policy, among other policy-related tasks.

The ADM module 140 can determine dependencies of applications of the network. That is, particular patterns of traffic can correspond to an application, and the interconnectivity or dependencies of the application can be mapped to generate a graph for the application (i.e., an application dependency mapping). In this context, an application refers to a set of networking components that provides connectivity for a given set of workloads. For example, in a conventional three-tier architecture for a web application, first endpoints of the web tier, second endpoints of the application tier, and third endpoints of the data tier make up the web application. The ADM module 140 can receive input data from various repositories of the data lake 130 (e.g., the flow attributes 132, the host and/or endpoint attributes 134, the process attributes 136, etc.). The ADM module 140 can analyze the input data to determine that there is first traffic flowing between external endpoints on port 80 of the first endpoints corresponding to Hypertext Transfer Protocol (HTTP) requests and responses. The input data can also indicate second traffic between first ports of the first endpoints and second ports of the second endpoints corresponding to application server requests and responses and third traffic flowing between third ports of the second endpoints and fourth ports of the third endpoints corresponding to database requests and responses. The ADM module 140 can define an ADM for the web application as a three-tier application including a first EPG comprising the first endpoints, a second EPG comprising the second endpoints, and a third EPG comprising the third endpoints.

The presentation module 112 can include an application programming interface (API) or command line interface (CLI) 160, a security information and event management (SIEM) interface 162, and a web front-end 164. As the analytics engine 110 processes network traffic and corresponding data and generates analytics data, the analytics data may not be in a human-readable form or it can be too voluminous for a user to navigate. The presentation module 112 can take the analytics data generated by analytics engine 110 and further summarize, filter, and organize the analytics data as well as create intuitive presentations for the analytics data.

In some embodiments, the API or CLI 160 can be implemented using Hadoop® Hive from Apache® for the back end, and Java® Database Connectivity (JDBC) from Oracle® Corporation of Redwood Shores, Calif., as an API layer. Hive is a data warehouse infrastructure that provides data summarization and ad hoc querying. Hive provides a mechanism to query data using a variation of structured query language (SQL) that is called HiveQL. JDBC is an API for the programming language Java®, which defines how a client can access a database.

In some embodiments, the SIEM interface 162 can be implemented using Hadoop® Kafka for the back end, and software provided by Splunk®, Inc. of San Francisco, Calif. as the SIEM platform. Kafka is a distributed messaging system that is partitioned and replicated. Kafka uses the concept of topics. Topics are feeds of messages in specific categories. In some embodiments, Kafka can take raw packet captures and telemetry information from the data mover 108 as input, and output messages to a SIEM platform, such as Splunk®. The Splunk® platform is utilized for searching, monitoring, and analyzing machine-generated data.

In some embodiments, the web front-end 164 can be implemented using software provided by MongoDB®, Inc. of New York, N.Y. and Hadoop® ElasticSearch from Apache® for the back-end, and Ruby on Rails™ as the web application framework. MongoDB® is a document-oriented NoSQL database based on documents in the form of JavaScript® Object Notation (JSON) with dynamic schemas. ElasticSearch is a scalable and real-time search and analytics engine that provides domain-specific language (DSL) full querying based on JSON. Ruby on Rails™ is model-view-controller (MVC) framework that provides default structures for a database, a web service, and web pages. Ruby on Rails™ relies on web standards such as JSON or extensible markup language (XML) for data transfer, and hypertext markup language (HTML), cascading style sheets, (CSS), and JavaScript® for display and user interfacing.

Although FIG. 1 illustrates an example configuration of the various components of a network traffic monitoring system, those of skill in the art will understand that the components of the network traffic monitoring system 100 or any system described herein can be configured in a number of different ways and can include any other type and number of components. For example, the sensors 104, the collectors 106, the data mover 108, and the data lake 130 can belong to one hardware and/or software module or multiple separate modules. Other modules can also be combined into fewer components and/or further divided into more components.

FIG. 2 illustrates an example of a network environment 200 in accordance with an embodiment. In some embodiments, a network traffic monitoring system, such as the network traffic monitoring system 100 of FIG. 1, can be implemented in the network environment 200. It should be understood that, for the network environment 200 and any environment discussed herein, there can be additional or fewer nodes, devices, links, networks, or components in similar or alternative configurations. Embodiments with different numbers and/or types of clients, networks, nodes, cloud components, servers, software components, devices, virtual or physical resources, configurations, topologies, services, appliances, deployments, or network devices are also contemplated herein. Further, the network environment 200 can include any number or type of resources, which can be accessed and utilized by clients or tenants. The illustrations and examples provided herein are for clarity and simplicity.

The network environment 200 can include a network fabric 202, a Layer 2 (L2) network 204, a Layer 3 (L3) network 206, and servers 208a, 208b, 208c, 208d, and 208e (collectively, 208). The network fabric 202 can include spine switches 210a, 210b, 210c, and 210d (collectively, “210”) and leaf switches 212a, 212b, 212c, 212d, and 212e (collectively, “212”). The spine switches 210 can connect to the leaf switches 212 in the network fabric 202. The leaf switches 212 can include access ports (or non-fabric ports) and fabric ports. The fabric ports can provide uplinks to the spine switches 210, while the access ports can provide connectivity to endpoints (e.g., the servers 208), internal networks (e.g., the L2 network 204), or external networks (e.g., the L3 network 206).

The leaf switches 212 can reside at the edge of the network fabric 202, and can thus represent the physical network edge. For instance, in some embodiments, the leaf switches 212d and 212e operate as border leaf switches in communication with edge devices 214 located in the external network 206. The border leaf switches 212d and 212e can be used to connect any type of external network device, service (e.g., firewall, deep packet inspector, traffic monitor, load balancer, etc.), or network (e.g., the L3 network 206) to the fabric 202.

Although the network fabric 202 is illustrated and described herein as an example leaf-spine architecture, one of ordinary skill in the art will readily recognize that various embodiments can be implemented based on any network topology, including any datacenter or cloud network fabric. Indeed, other architectures, designs, infrastructures, and variations are contemplated herein. For example, the principles disclosed herein are applicable to topologies including three-tier (including core, aggregation, and access levels), fat tree, mesh, bus, hub and spoke, etc. Thus, in some embodiments, the leaf switches 212 can be top-of-rack switches configured according to a top-of-rack architecture. In other embodiments, the leaf switches 212 can be aggregation switches in any particular topology, such as end-of-row or middle-of-row topologies. In some embodiments, the leaf switches 212 can also be implemented using aggregation switches.

Moreover, the topology illustrated in FIG. 2 and described herein is readily scalable and can accommodate a large number of components, as well as more complicated arrangements and configurations. For example, the network can include any number of fabrics 202, which can be geographically dispersed or located in the same geographic area. Thus, network nodes can be used in any suitable network topology, which can include any number of servers, virtual machines or containers, switches, routers, appliances, controllers, gateways, or other nodes interconnected to form a large and complex network. Nodes can be coupled to other nodes or networks through one or more interfaces employing any suitable wired or wireless connection, which provides a viable pathway for electronic communications.

Network communications in the network fabric 202 can flow through the leaf switches 212. In some embodiments, the leaf switches 212 can provide endpoints (e.g., the servers 208), internal networks (e.g., the L2 network 204), or external networks (e.g., the L3 network 206) access to the network fabric 202, and can connect the leaf switches 212 to each other. In some embodiments, the leaf switches 212 can connect endpoint groups (EPGs) to the network fabric 202, internal networks (e.g., the L2 network 204), and/or any external networks (e.g., the L3 network 206). EPGs are groupings of applications, or application components, and tiers for implementing forwarding and policy logic. EPGs can allow for separation of network policy, security, and forwarding from addressing by using logical application boundaries. EPGs can be used in the network environment 200 for mapping applications in the network. For example, EPGs can comprise a grouping of endpoints in the network indicating connectivity and policy for applications.

As discussed, the servers 208 can connect to the network fabric 202 via the leaf switches 212. For example, the servers 208a and 208b can connect directly to the leaf switches 212a and 212b, which can connect the servers 208a and 208b to the network fabric 202 and/or any of the other leaf switches. The servers 208c and 208d can connect to the leaf switches 212b and 212c via the L2 network 204. The servers 208c and 208d and the L2 network 204 make up a local area network (LAN). LANs can connect nodes over dedicated private communications links located in the same general physical location, such as a building or campus.

The WAN 206 can connect to the leaf switches 212d or 212e via the L3 network 206. WANs can connect geographically dispersed nodes over long-distance communications links, such as common carrier telephone lines, optical light paths, synchronous optical networks (SONET), or synchronous digital hierarchy (SDH) links. LANs and WANs can include L2 and/or L3 networks and endpoints.

The Internet is an example of a WAN that connects disparate networks throughout the world, providing global communication between nodes on various networks. The nodes typically communicate over the network by exchanging discrete frames or packets of data according to predefined protocols, such as the Transmission Control Protocol/Internet Protocol (TCP/IP). In this context, a protocol can refer to a set of rules defining how the nodes interact with each other. Computer networks can be further interconnected by an intermediate network node, such as a router, to extend the effective size of each network. The endpoints, e.g. the servers 208, can include any communication device or component, such as a computer, server, blade, hypervisor, virtual machine, container, process (e.g., running on a virtual machine), switch, router, gateway, host, device, external network, etc.

In some embodiments, the network environment 200 also includes a network controller running on the host 208a. The network controller is implemented using the Application Policy Infrastructure Controller (APIC™) from Cisco®. The APIC™ provides a centralized point of automation and management, policy programming, application deployment, and health monitoring for the fabric 202. In some embodiments, the APIC™ is operated as a replicated synchronized clustered controller. In other embodiments, other configurations or software-defined networking (SDN) platforms can be utilized for managing the fabric 202.

In some embodiments, a physical server 208 can have instantiated thereon a hypervisor 216 for creating and running one or more virtual switches (not shown) and one or more virtual machines 218, as shown for the host 208b. In other embodiments, physical servers can run a shared kernel for hosting containers. In yet other embodiments, the physical server 208 can run other software for supporting other virtual partitioning approaches. Networks in accordance with various embodiments can include any number of physical servers hosting any number of virtual machines, containers, or other virtual partitions. Hosts can also comprise blade/physical servers without virtual machines, containers, or other virtual partitions, such as the servers 208a, 208c, 208d, and 208e.

The network environment 200 can also integrate a network traffic monitoring system, such as the network traffic monitoring system 100 shown in FIG. 1. For example, the network traffic monitoring system of FIG. 2 includes sensors 220a, 220b, 220c, and 220d (collectively, “220”), collectors 222, and an analytics engine, such as the analytics engine 110 of FIG. 1, executing on the server 208e. The analytics engine can receive and process network traffic data collected by the collectors 222 and detected by the sensors 220 placed on nodes located throughout the network environment 200. Although the analytics engine 208e is shown to be a standalone network appliance in FIG. 2, it will be appreciated that the analytics engine 208e can also be implemented as a virtual partition (e.g., VM or container) that can be distributed onto a host or cluster of hosts, software as a service (SaaS), or other suitable method of distribution. In some embodiments, the sensors 220 run on the leaf switches 212 (e.g., the sensor 220a), the hosts (e.g., the sensor 220b), the hypervisor 216 (e.g., the sensor 220c), and the VMs 218 (e.g., the sensor 220d). In other embodiments, the sensors 220 can also run on the spine switches 210, virtual switches, service appliances (e.g., firewall, deep packet inspector, traffic monitor, load balancer, etc.) and in between network elements. In some embodiments, sensors 220 can be located at each (or nearly every) network component to capture granular packet statistics and data at each hop of data transmission. In other embodiments, the sensors 220 may not be installed in all components or portions of the network (e.g., shared hosting environment in which customers have exclusive control of some virtual machines).

As shown in FIG. 2, a host can include multiple sensors 220 running on the host (e.g., the host sensor 220b) and various components of the host (e.g., the hypervisor sensor 220c and the VM sensor 220d) so that all (or substantially all) packets traversing the network environment 200 can be monitored. For example, if one of the VMs 218 running on the host 208b receives a first packet from the WAN 206, the first packet can pass through the border leaf switch 212d, the spine switch 210b, the leaf switch 212b, the host 208b, the hypervisor 216, and the VM. Since all or nearly all of these components contain a respective sensor, the first packet will likely be identified and reported to one of the collectors 222. As another example, if a second packet is transmitted from one of the VMs 218 running on the host 208b to the host 208d, sensors installed along the data path, such as at the VM 218, the hypervisor 216, the host 208b, the leaf switch 212b, and the host 208d will likely result in capture of metadata from the second packet.

The present includes systems, methods, and computer-readable media for automatically identifying configurations of middleboxes using stitched traffic flows at the middleboxes. In particular, flow records of traffic flow segments at a middlebox in a network environment can be collected. The flow records can include transaction identifiers assigned to the traffic flow segments. Sources and destinations of the traffic flow segments of the traffic flow segments in the network environment with respect to the middlebox can be determined using the flow records. Further, the traffic flow segments can be stitched together to form a plurality of stitched traffic flows at the middlebox using either or both the transaction identifiers assigned to the traffic flow segments and the sources and destinations of the traffic flow segments identified from the flow records. The stitched traffic flows can pass through the middlebox ultimately between one or more clients and one or more servers as client-server switched traffic flows between the one or more client and the one or more servers. A configuration of the middlebox operating in the network environment can be identified based on the stitched traffic flows at the middlebox in the network environment. The configuration of the middlebox can include the configuration of the middlebox operating to facilitate network service access to the one or more clients in the network environment through the one or more servers.

FIG. 3 depicts a diagram of an example network environment 300 for stitching together traffic flow segments across multiple nodes between a client and a server. The network environment 300 shown in FIG. 3 includes a client 302, a server 304, a first middlebox 306-1, and a second middlebox 306-2 (herein referred to as “the middleboxes 306”). The client 302 and the server 304 can exchange data. More specifically, the client 302 and the server 304 can exchange data as part of the client 302 accessing network services in the network environment 300 and as part of the server 304 providing the client 302 access to network services in the network environment 300. For example, the client 302 can send a request for data that can ultimately be delivered to the server 304 and the server 304 can ultimately send the data back to the client 302 as part of a reply to the request.

The client 302 and the server 304 can exchange data as part of one or more traffic flows. A traffic flow can be unidirectional or bidirectional. For example, a traffic flow can include the client 302 sending a request that is ultimately received at the server 304. Vice versa, a traffic flow can include the server 304 sending a response that is ultimately received at the client 302. In another example, a traffic flow can include both the client 302 sending a request that is ultimately received at the server 304 and the server 304 sending a response to the request that is ultimately received at the client 302. Traffic flows between the client 302 and the server 304 can form part of a traffic flow including the client 302 and other sources/destinations at different network nodes, e.g. separate from the server 304 and the client 302, within a network environment. For example, traffic flows between the client 302 and the server 304 can form part of an overall traffic flow between the client 302 and a network node within a network environment that is ultimately accessed through the server 304 and a network fabric.

Further, the client 302 and the server 304 can exchange data as part of one or more transactions. A transaction can include a request originating at the client 302 and ultimately sent to the server 304. Further, a transaction can include a response to the request originating at the server 304 and ultimately send to the client 302. Vice versa, a transaction can include a request that originates at the server 304 and is ultimately sent to the client 302 and a response to the request that originates at the client 302 and is ultimately send to the server 304. A transaction can be associated with a completed flow. Specifically, a completed flow of a transaction can include a request that originates at the client 302 and is sent to the server 304 and a response to the request that originates at the server 304 and is sent to the client 302.

In the example network environment 300 shown in FIG. 3, the client 302 and the server 304 can exchange data or otherwise communicate through the middleboxes 306. A middlebox, as used herein, is an applicable networking device for controlling network traffic in the network environment 300 that passes through the middlebox. More specifically, a middlebox can be an applicable networking device for filtering, inspecting, modifying, or otherwise controlling traffic that passes through the middlebox for purposes other than actually forwarding the traffic to an intended destination. For example, a middlebox can include a firewall, an intrusion detection system, a network address translator, a WAN optimizer, and a load balancer.

In supporting exchange of data between the client 302 and the server 304, different portions of traffic flows, otherwise referred to as traffic flow segments, can be created at the middleboxes 306 between the client 302 and the server 304. Specifically, the first middlebox 306-1 can receive data from the client 302 in a first traffic flow segment 308-1. Subsequently, the first middlebox 306-1 can provide data received from the client 302, e.g. through the first traffic flow segment 308-1, to the second middlebox 306-2 as part of a second traffic flow segment 308-2. The second middlebox 306-2 can then provide the data received from the client 302 through the first middlebox 306-1, e.g. through the second traffic flow segment 308-2, to the server 304 as part of a third traffic flow segment 308-3. Similarly, the second middlebox 306-2 can receive data from the server 304 in a fourth traffic flow segment 308-4. The second middlebox 306-2 can provide data received from the server 304, e.g. in the fourth traffic flow segment 308-4, to the first middlebox 306-1 in a fifth traffic flow segment 308-5. Subsequently, the first middlebox 306-1 can provide data from the server 304 and received from the second middlebox in the fifth traffic flow segment 308-5 to the client 302 as part of a sixth traffic flow segment 308-6.

All or an applicable combination of the first traffic flow segment 308-1, the second traffic flow segment 308-2, the third traffic flow segment 308-3, the fourth traffic flow segment 308-4, the fifth traffic flow segment 308-5, and the sixth traffic flow segment 308-6 (collectively referred to as the “traffic flow segments 308”) can form part of a single traffic flow. For example, the first traffic flow segment 308-1, the second traffic flow segment 308-2, and the third traffic flow segment 308-3 can be used to transmit a request from the client 302 to the server 304 and combine to form a single traffic flow between the client 302 and the server 304. In another example, the first, second, and third traffic flow segments 308-1, 308-2, 308-3 can form a request transmitted to the server 304 and the fourth, fifth, and sixth traffic flow segments 308-4, 308-5, and 308-6 can form a response to the request. Further in the example, the traffic flow segments 308 including both the request and the response to the request can form a single traffic flow between the client 302 and the server 304.

The traffic flow segments 308 can be associated with or otherwise assigned one or more transaction identifiers. More specifically, a transaction identifier can be uniquely associated with a single traffic flow passing through both the first middlebox 306-1 and the second middlebox 306-2. Subsequently, all or a combination of the traffic flow segments 308 can be associated with a transaction identifier uniquely associated with a traffic flow formed by all or the combination of the traffic flow segments 308. For example, the traffic flow segments 308 can form a single traffic flow between the client 302 and the server 304 and each be assigned a single transaction identifier for the traffic flow. In another example, the first traffic flow segment 308-1, the second traffic flow segment 308-2, and the third traffic flow segment 308-3 can form a first traffic flow and the fourth traffic flow segment 308-4, the fifth traffic flow segment 308-5, and the sixth traffic flow segment 308-6 can form a second traffic flow. Subsequently, a transaction identifier uniquely associated with the first traffic flow can be assigned to the first traffic flow segment 308-1, the second traffic flow segment 308-2, and the third traffic flow segment 308-3, while a transaction identifier uniquely associated with the second traffic flow can be assigned to the fourth traffic flow segment 308-4, the fifth traffic flow segment 308-5, and the sixth traffic flow segment 308-6.

While the client 302 and the server 304 are shown as communicating through the middleboxes 306, in the example environment shown in FIG. 3, either or both of the client 302 and the server 304 can be replaced with another middlebox. For example, the server 304 and another middlebox can communicate with each other through the middleboxes 306. In another example, the client 302 and another middlebox can communicate with each other through the middleboxes 306.

The example network environment 300 shown in FIG. 3 includes a first middlebox traffic flow segment collector 310-1, a second middlebox traffic flow segment collector 310-2 (herein referred to as “middlebox traffic flow segment collectors 310”), and a middlebox traffic flow stitching system 312. The middlebox traffic flow segment collectors 310 function to collect flow records for the middleboxes 306. In collecting flow records for the middleboxes 306, the middlebox traffic flow segment collectors 310 can be implemented as an appliance. Further, the middlebox traffic flow segment collectors 310 can be implemented, at least in part, at the middleboxes 306. Additionally, the middlebox traffic flow segment collectors 310 can be implemented, at least in part, remote from the middleboxes 306. For example, the middlebox traffic flow segment collectors 310 can be implemented on virtual machines either residing at the middleboxes 306 or remote from the middleboxes 306. While, the example network environment 300 in FIG. 3 is shown to include separate middlebox traffic flow segment collectors, in various embodiments, the environment 300 can only include a single middlebox traffic flow segment collector. More specifically, the environment 300 can include a single middlebox traffic flow segment collector that collects flow records from both the first middlebox 306-1 and the second middlebox 306-2.

Each of the middlebox traffic flow segment collectors 310 can collect flow records for a corresponding middlebox. For example, the first middlebox traffic flow segment collector 310-1 can collect flow records for the first middlebox 306-1. Further in the example, the second middlebox traffic flow segment collector 310-2 can collect flow records for the second middlebox 306-2.

Flow records of a middlebox can include applicable data related to traffic segments flowing through the middlebox. Specifically, flow records of a middlebox can include one or a combination of a source of data transmitted in a traffic flow segment, a destination for data transmitted in a traffic flow segment, a transaction identifier assigned to a traffic flow segment. More specifically, flow records of a middlebox can include one or a combination of an address, e.g. IP address of a source or a destination, and an identification of a port at a source or a destination, e.g. an ephemeral port, a virtual IP (herein referred to as “VIP”) port, a subnet IP (herein referred to as “SNIP”) port, or a server port. For example, flow records collected by the first middlebox traffic flow segment collector 310-1 for the first traffic flow segment 308-1 and the second traffic flow segment 308-2 can include a unique identifier associated with a traffic flow formed by the segments 308-1 and 308-2 and assigned to the first and second traffic flow segments 308-1 and 308-2. Further in the example, the flow records can include an IP address of the client 302 where the first traffic flow segment 308-1 originates and a VIP port at the first middlebox 306-1 where the first traffic flow segment 308-1 is received. Still further in the example, the flow records can include an SNIP port at the first middlebox 306-1 where the second traffic flow segment 308-2 originates and a VIP port at the second middlebox 306-2 where the second traffic flow segment 308-2 is received, e.g. for purposes of load balancing.

Data included in flow records of corresponding traffic flow segments passing through a middlebox can depend on whether the traffic flow segments originate at a corresponding middlebox of the middleboxes 306 or ends at a corresponding middlebox of the middlebox 306. For example, a flow record for the first traffic flow segment 308-1 that is collected by the first middlebox traffic flow segment collector 310-1 can include a unique transaction identifier and indicate that the first traffic flow segment starts at the client 302 and ends at the first middlebox 306-1. Similarly, a flow record for the second traffic flow segment 308-2 can include the unique transaction identifier, which is also assigned to the first traffic flow segment 308-1, as well as an indication that the second traffic flow segment starts at the first middlebox 306-1 and ends at the second middlebox 306-2. Accordingly, flow records for traffic flow segments passing through the middleboxes 306 are each rooted at the middleboxes 306, e.g. by including an indication of one of the middleboxes 306 as a source or a destination of the traffic flow segments. Specifically, as traffic flow segments are rooted at the middleboxes 306, flow records for the traffic flow segments passing through the middleboxes 306 each either begin or end at the middleboxes 306.

The middlebox es306 can generate flow records for traffic flow segments passing through the middleboxes 306. More specifically, the middleboxes 306 can associate or otherwise assign a unique transaction identifier to traffic flow segments as part of creating flow records for the traffic flow segments. For example, the first middlebox 306-1 can assign a TID1 of a consumer request to the first traffic flow segment 308-1 as part of creating a flow record, e.g. for the first traffic flow segment 308-1. Further in the example, the first middlebox 306-1 can determine to send the consumer request to the second middlebox 306-2 and/or the server 304, e.g. as part of load balancing. Still further in the example, the first middlebox 306-1 can assign the TID1 of the consumer request to the second traffic flow segment 308-1 as the consumer request is transmitted to the second middlebox 306-2 through the second traffic flow segment 308-2, e.g. as part of the load balancing. Subsequently, the middleboxes 306 can export the generated flow segments for traffic flow segments passing through the middleboxes 306.

Additionally, the middleboxes 306 can modify a flow record for a traffic flow segment by associating the traffic flow segment with a transaction identifier as part of exporting the flow record. For example, the middleboxes 306 can determine to export a flow record for a traffic flow segment. Subsequently, before exporting the flow record, the middleboxes 306 can associate the traffic flow segment with a transaction identifier and subsequently modify the flow record to include the transaction identifier. The middleboxes 306 can then export the modified flow record including the transaction identifier.

The middlebox traffic flow segment collectors 310 can collect flow records from the middleboxes 306 as the flow records are completed or otherwise generated by the middleboxes 306. Specifically, the middleboxes 306 can generate and/or export flow records for traffic flow segments as all or portions of corresponding traffic flows actually pass through the middleboxes 306. More specifically, the first middlebox 306-1 can create and export traffic flow records as either or both the first traffic flow segment 308-1 and the second traffic flow segment 308-2 are completed at the first middlebox 306-1. Additionally, the middleboxes 306 can generate and/or export flow records for traffic flow segments once a corresponding traffic flow formed by the segments is completed through the middleboxes 306. For example, the first middlebox 306-1 can create and export traffic flow records for the traffic flow segments 308-1, 308-2, 308-5, and 308-6 once all of the traffic flow segments are transmitted to complete a traffic flow through the first middlebox 306-1. Further in the example, the first middlebox 306-1 can recognize that a consumer to producer flow is complete, e.g. the first, second, and third traffic flow segments 308-1, 308-2,308-3 are complete or all of the traffic flow segments 308 are completed, and subsequently the first middlebox 306-1 can export one or more corresponding flow records to the first middlebox traffic flow segment collector 310-1.

The middlebox traffic flow segment collectors 310 can receive or otherwise collect traffic flow records from the middleboxes 306 according to an applicable protocol for exporting flow records, e.g. from a middlebox. More specifically, the middleboxes 306 can export flow records to the middlebox traffic flow segment collectors 310 according to an applicable protocol for exporting flow records, e.g. from a middlebox. For example, the middleboxes 306 can export flow records to the middlebox traffic flow segment collector 310 using an Internet Protocol Flow Information Export (herein referred to as “IPFIX”) protocol. In another example, the middleboxes 306 can export flow records to the middlebox traffic flow segment collectors 310 using a NetFlow Packet transport protocol.

While flow records can indicate traffic flow segments are rooted at the middleboxes 306, the flow records for traffic segments passing through the middlebox 306 can fail to link the traffic flow segments, e.g. through the middleboxes 306. Specifically, flow records for the first traffic flow segment 308-1 can indicate that the first traffic flow segment 308-1 ends at the first middlebox 306-1 while flow records for the second traffic flow segment 308-2 can indicate that the second traffic flow segment 308-2 begins at the first middlebox 306-1, while failing to link the first and second traffic flow segments 308-1 and 308-2. Further, flow records for the first traffic flow segment 308-1 can indicate that the third traffic flow segment 308-3 begins at the second middlebox 306-2, while failing to link the first and second traffic flow segments 308-1 and 308-2 with the third traffic flow segment 308-3.

Failing to link traffic flow segments through the middleboxes 306 is problematic in synchronizing or otherwise identifying how the server 304 and the client 302 communicate through the middleboxes 306. Specifically, failing to link traffic flow segments through the middleboxes 306 leads to a view from a server-side perspective that all flows end in the second middlebox 306-2. Similarly, failing to link traffic flow segments through the middleboxes 306 leads to a view from a client-side perspective that all flows end in the first middlebox 306-1. This can correspond to gaps in mapping traffic flows between the client 302 and the server 304, e.g. the middleboxes 306 are treated like a black box without linking the client 302 with the server 304. In turn, this can lead to deficiencies in diagnosing problems within the network environment 300. For example, a failed policy check at the first middlebox 306-1 can mistakenly be identified as happening at the client 302 even though it actually occurs at the first middlebox 306-1. Specifically, the failed policy check can be triggered by a failure of the first middlebox 306-1 to route data according to the policy between the client 302 and the server 304, however since the traffic flow segments rotted at the first middlebox 306-1 are not linked with the traffic flow segments rotted at the second middlebox 306-2, the failed policy check can be identified from a traffic flow segment as occurring at the client 302 instead of the first middlebox 306-1.

The middlebox traffic flow stitching system 312 functions to stitch together traffic flow segments passing through the middleboxes 306 to create stitched traffic flows at the middleboxes 306. For example, the middlebox traffic flow stitching system 312 can stitch together the first traffic flow segment 308-1, the second traffic flow segment 308-2, the fifth traffic flow segment 308-5, and the sixth traffic flow segment 308-6 to form a stitched traffic flow at the first middlebox 306-1. Similarly, the middlebox traffic flow stitching system 312 can stitch together the second traffic flow segment 308-2, the third traffic flow segment 308-3, the fourth traffic flow segment 308-4, and the fifth traffic flow segment 308-5 to form a stitched traffic flow at the second middlebox 306-2. Stitched traffic flows can be represented or otherwise used to create corresponding flow data. Flow data for a stitched traffic flow can include identifiers of stitched traffic flow segments, e.g. identifiers of sources and destinations of the traffic flow segments, and transactions associated with the stitched traffic flow segments, e.g. associated transaction identifiers.

Further, the middlebox traffic flow stitching system 312 can stitch together stitched traffic flows to form a cross-middlebox stitched traffic flow. A cross-middlebox stitched traffic flow can include traffic flow segments stitched together across a plurality of corresponding middleboxes that the traffic flow segments pass through or are otherwise rooted at to create a stitched traffic flow across the middleboxes. For example, the middlebox traffic flow stitching system 312 can stitch together the first, second, fifth, and sixth traffic flow segments 308-1, 308-2, 308-5, and 308-6 to form a first stitched traffic flow at the first middlebox 306-1. Further in the example, the middlebox traffic flow stitching system 312 can stitch together the second, third, fourth, and fifth traffic flow segments 308-2, 308-3, 308-4, and 308-5 to form a second stitched traffic flow at the second middlebox 306-2. Still further in the example, the middlebox traffic flow stitching system 312 can stitch together the first stitched traffic flow and the second stitched traffic flow to form a cross-middlebox stitched traffic flow between the client 302 and the server 304 that spans across the first middlebox 306-1 and the second middlebox 306-2.

In stitching together traffic flow segments at the middleboxes 306 to create stitched traffic flows and corresponding cross-middlebox stitched traffic flows, the middleboxes 306 can no longer function as black boxes with respect to traffic flows passing through the middleboxes 306. Specifically, from both a server side perspective and a client side perspective, a traffic flow can be viewed as actually passing through the middleboxes 306 to the client 302 or the server 304 and not just as traffic flow segments that only originate at or end at the middleboxes 306. This is advantageous as it allows for more complete and insightful network monitoring, leading to more accurate problem diagnosing and solving. For example, as traffic flows are seen as actually passing through the middleboxes 306, misconfigurations of the middleboxes 306 can be identified from the traffic flows, e.g. as part of monitoring network environments.

Traffic flows at the middleboxes 306 stitched together by the middlebox traffic flow stitching system 312 can be used to enforce policies at middleboxes, including the middleboxes 306. Specifically, stitched traffic flows and corresponding cross-middlebox stitched traffic flows generated by the middlebox traffic flow stitching system 312 can be used to identify dependencies between either or both servers and clients. For example, cross-middlebox stitched traffic flows generated by the middlebox traffic flow stitching system 312 can be used to generate application dependency mappings between different applications at servers and clients. Subsequently, policies can be set and subsequently enforced at the middleboxes 306 based on dependencies identified using the cross-middlebox stitched traffic flows generated by the middlebox traffic flow stitching system 312. For example, a policy to load balance communications between clients and servers can be identified according to an application dependency mapping between the clients and the servers identified through cross-middlebox stitched traffic flows at the middleboxes 306. Further in the example, the policy can subsequently be enforced at the middleboxes 306 to provide load balancing between the clients and the servers.

The middlebox traffic flow stitching system 312 can stitch together traffic flow segments passing through the middleboxes 306 based on flow records collected from the middleboxes 306. Specifically, the middlebox traffic flow stitching system 312 can stitch together traffic flow segments based on flow records collected by the middlebox traffic flow segment collectors 310 from the middleboxes 306. In using flow records to stitch together traffic flow segments, the middlebox traffic flow stitching system 312 can stitch together traffic flow segments based on transaction identifiers assigned to the traffic flow segments, as indicated by the flow records. Specifically, the middlebox traffic flow stitching system 312 can stitch together traffic flow segments that are assigned the same transaction identifiers. For example, the traffic flow segments 308 can all have the same assigned transaction identifier, and the middlebox traffic flow stitching system 312 can stitch together the traffic flow segments 308 to form corresponding first and second stitched traffic flows based on the shared transaction identifier. Further in the example, the middlebox traffic flow stitching system 312 can stitch together the traffic flow segments 308 to form a corresponding first stitched traffic flow about the first middlebox 306-1 and a corresponding second stitched traffic flow about the second middlebox 306-2.

The middlebox traffic flow stitching system 312 can stitch together stitched traffic flows to form a cross-middlebox stitched traffic flow based on flow records collected from the middleboxes 306. Specifically, the middlebox traffic flow stitching system 312 can stitch together stitched traffic flows based on one or more transaction identifiers included as part of the flow records to form a cross-middlebox stitched traffic flow. More specifically, the middlebox traffic flow stitching system 312 can stitch together stitched traffic flows based on one or more transaction identifiers associated with the traffic flow segments and used to generate the stitched traffic flows from the traffic flow segments. For example, the middlebox traffic flow stitching system 312 can stitch together the traffic flow segments 308 based on a transaction identifier associated with the traffic flow segments 308 to form a first stitched traffic flow at the first middlebox 306-1 and a second stitched traffic flow at the second middlebox 306-2.

Further, the middlebox traffic flow stitching system 312 can identify flow directions of traffic flow segments passing through the middlebox 306 using flow records collected from the middlebox 306 by the middlebox traffic flow segment collector 310. Specifically, the middlebox traffic flow stitching system 312 can identify flow directions of traffic flow segments with respect to the middlebox 306 using flow records collected from the middlebox 306. For example, the middlebox traffic flow stitching system 312 can use flow records from the middlebox 306 to identify the fourth traffic flow segment 308-4 flows from the middlebox 306 to the client 302. The middlebox traffic flow stitching system 312 can use identified sources and destinations of traffic flow segments, as indicated by flow records, to identify flow directions of the traffic flow segments. For example, the middlebox traffic flow stitching system 312 can determine the first traffic flow segment 308-1 flows from the client 302 to the middlebox 306 based on an identification of a client IP address as the source of the first traffic flow segment 308-1 and an identification of a VIP port at the middlebox 306.

In stitching together traffic flow segments based on flow records, the middlebox traffic flow stitching system 312 can stitch together the traffic flow segments based on directions of the traffic flow segments identified from the flow records. For example, the middlebox traffic flow stitching system 312 can stitch together the first traffic flow segment 308-1 with the second traffic flow segment 308-2 based on the identified direction of the first and second traffic flow segments 308-1 and 308-2 from the client 302 towards the server 304. Additionally, in stitching together traffic flow segments based on flow records, the middlebox traffic flow stitching system 312 can stitch together the traffic flow segments based on directions of the traffic flow segments and also transaction identifiers assigned to the traffic flow segments. For example, the middlebox traffic flow stitching system 312 can stitch the third and fourth traffic flow segments 308-3 and 308-4 together based on the segments having the same transaction identifier and the shared direction of the segments from the server 304 to the client 302.

The middlebox traffic flow stitching system 312 can stitch together traffic flow segments in an order based on flow directions of the traffic flow segments. More specifically, the middlebox traffic flow stitching system 312 can use a shared transaction identifier to determine traffic flow segments to stitch together, and stitch the traffic flow segments in a specific order based on flow directions of the traffic flow segments to form a stitched traffic flow. For example, the middlebox traffic flow stitching system 312 can determine to stitch the traffic flow segments 308 based on a shared transaction identifier assigned to the traffic flow segments 308. Further in the example, the middlebox traffic flow stitching system 312 can determine to stitch the second traffic flow segment 308-2 after the first traffic flow segment 308-1, stitch the fifth traffic flow segment 308-5 after the second traffic flow segment 308-2, and stitch the sixth traffic flow segment 308-6 after the fifth traffic flow segment 308-5, based on corresponding identified flow directions of the traffic flow segments 308, e.g. with respect to the middlebox 306.

Further, the middlebox traffic flow stitching system 312 can stitch together stitched traffic flows to form cross-middlebox stitched traffic flows based on directions of traffic flow segments used to form the stitched traffic flows. For example, the middlebox traffic flow stitching system 312 can stitch together a stitched traffic flow at the first middlebox 306-1 with a stitched traffic flow at the second middlebox 306-2 based on the second traffic flow segment 308-2 having a direction from the first middlebox 306-1 to the second middlebox 306-2. Similarly, the middlebox traffic flow stitching system 312 can stitch together a stitched traffic flow at the second middlebox 306-2 with a stitched traffic flow at the first middlebox 306-1 based on the fifth traffic flow segment having a direction from the second middlebox 306-2 to the first middlebox 306-1.

Additionally, the middlebox traffic flow stitching system 312 can stitch together stitched traffic flows to form cross-middlebox stitched traffic flows based on common traffic flow segments. Specifically, the middlebox traffic flow stitching system 312 can stitch together stitched traffic flows to form cross-middlebox stitched traffic flows if the stitched traffic flows share one or more common traffic flow segments. A common traffic flow segment is a traffic flow segment that is used to form two separate stitched traffic flows. For example, the second traffic flow segment 308-2 can be a common traffic flow segment for a stitched traffic flow at the first middlebox 306-1 and a stitched traffic flow at the second middlebox 306-2. Further, the middlebox traffic flow stitching system 312 can stitch together flows according to positions of common traffic flow segments in the stitched traffic flows. For example, based on the position of the second traffic flow segment 308-2 in a first stitched traffic flow at the first middlebox 306-1 and a second stitched traffic flow at the second middlebox 306-2, the middlebox traffic flow stitching system 312 can stitch the second stitched traffic flow after the first stitched traffic flow.

The middlebox traffic flow stitching system 312 can incorporate either or both stitched traffic flows through the middleboxes 306 and cross-middlebox stitched traffic flows as part of network traffic data for the network environment. For example, the middlebox traffic flow stitching system 312 can include stitched traffic flows through the middleboxes 306 and cross-middlebox stitched traffic flows with other traffic flows in the network environment, e.g. from servers to nodes in a network fabric. In another example, the middlebox traffic flow stitching system 312 can update network traffic data to indicate a completed flow between a client and a server passes between the server and the client through multiple middleboxes, e.g. as indicated by a cross-middlebox stitched traffic flow.

The middlebox traffic flow stitching system 312 can incorporate stitched traffic flows and cross-middlebox stitched traffic flows as part of network traffic data generated by an applicable network traffic monitoring system, such as the network traffic monitoring system 100 shown in FIG. 1. In incorporating stitched traffic flows and cross-middlebox stitched traffic flows with network traffic data generated by a network traffic monitoring system, all or portions of the middlebox traffic flow stitching system 312 can be integrated at the network traffic monitoring system. For example, a portion of the middlebox traffic flow stitching system 312 implemented at a network traffic monitoring system can received stitched traffic flows and cross-middlebox stitched traffic flows from a portion of the middlebox traffic flow stitching system 312 implemented at the middlebox traffic flow segment collectors 310. Subsequently, the middlebox traffic flow stitching system 312, e.g. implemented at the network traffic monitoring system, can incorporate the stitched traffic flows and the cross-middlebox stitched traffic flows into network traffic data generated by the network traffic monitoring system.

In incorporating stitched traffic flows and cross-middlebox stitched traffic flows into network traffic data, the middlebox traffic flow stitching system 312 can extend network traffic flows in the network traffic data based on the stitched traffic flows and cross-middlebox stitched traffic flows. Specifically, the middlebox traffic flow stitching system 312 can stitch already stitched traffic flows extending through the middleboxes 306 to the client with other stitched traffic flows extending into the network environment 300. More specifically, the middlebox traffic flow stitching system 312 can stitch already stitched traffic flows through the middleboxes 306 with other traffic flows that extend from the server 304 to other servers or nodes in the network environment 300. For example, the middlebox traffic flow stitching system 312 can stitch together a traffic flow extending from a network fabric to the server 304 with a stitched traffic flow through the middleboxes 306 to the client 302. This can create a completed traffic flow from the network fabric to the client 302 through the middleboxes 306.

FIG. 4 illustrates a flowchart for an example method of forming a cross-middlebox stitched traffic flow across middleboxes. The method shown in FIG. 4 is provided by way of example, as there are a variety of ways to carry out the method. Additionally, while the example method is illustrated with a particular order of blocks, those of ordinary skill in the art will appreciate that FIG. 4 and the blocks shown therein can be executed in any order and can include fewer or more blocks than illustrated.

Each block shown in FIG. 4 represents one or more steps, processes, methods or routines in the method. For the sake of clarity and explanation purposes, the blocks in FIG. 4 are described with reference to the network environment shown in FIG. 3.

At step 400, the middlebox traffic flow segment collectors 310 collects flow records of traffic flow segments at first and second middleboxes in a network environment corresponding to one or more traffic flows passing through the middleboxes. The flow records can include one or more transaction identifiers assigned to the traffic flow segments. The flow records of the traffic flow segments at the middleboxes can be generated by the middleboxes and subsequently exported to the middlebox traffic flow segment collectors 310. More specifically, the flow records can be exported to the middlebox traffic flow segment collectors 310 through the IPFIX protocol. The flow records can be exported to the middlebox traffic flow segment collectors 310 after each of the traffic flow segments is established, e.g. through the middleboxes. Alternatively, the flow records can be exported to the middlebox traffic flow segment collectors 310 after a corresponding traffic flow of the traffic flow segments is completed, e.g. through the middleboxes.

At step 402, the middlebox traffic flow stitching system 312 identifies sources and destinations of the traffic flow segments in the network environment with respect to the middleboxes using the flow records. For example, the middlebox traffic flow stitching system 312 can identify whether a traffic flow segment is passing from a client to one or the middleboxes towards a server using the flow records. In another example, the middlebox traffic flow stitching system 312 can identify whether a traffic flow segment is passing from a server to the middleboxes towards a client using the flow records. In yet another example, the middlebox traffic flow stitching system 312 can identify whether a traffic flow segment is passing between the middleboxes, e.g. from the first middlebox to the second middlebox or vice versa. The middlebox traffic flow stitching system 312 can identify flow directions of the traffic flow segments based on either or both sources and destinations of the traffic flow segments included as part of the flow records. For example, the middlebox traffic flow stitching system 312 can identify a flow direction of a traffic flow segment based on an IP address of a server where the flow segment started and a SNIP port on one of the first or second middleboxes that ends the flow segment at the middlebox.

At step 404, the middlebox traffic flow stitching system 312 stitches together the traffic flow segments to form a first stitched traffic flow at the first middlebox and a second stitched traffic flow at the second middlebox. More specifically, the middlebox traffic flow stitching system 312 can stitch together the traffic flow segments to form a first stitched traffic flow at the first middlebox and a second stitched traffic flow at the second middlebox based on one or more transaction identifiers assigned to the traffic flow segments. Further, the middlebox traffic flow stitching system 312 can stitch together the traffic flow segments to form a first stitched traffic flow at the first middlebox and a second stitched traffic flow at the second middlebox based on the sources and destinations of the traffic flow segments with respect to the middleboxes. For example, the traffic flow segments sharing the same transaction identifier can be stitched together based on the directions of the traffic flow segments to form the first and second stitched traffic flows, e.g. based on the flow records. More specifically, the one or more transaction identifiers assigned to the traffic flow segments can be indicated by the flow records collected at step 400 and subsequently used to stitch the traffic flow segments together to form the first and second stitched traffic flows.

At step 406, the middlebox traffic flow stitching system 312 stitches together the first stitched traffic flow and the second stitched traffic flow to from a cross-middlebox stitched traffic flow across the first middlebox and the second middlebox. Specifically, the middlebox traffic flow stitching system 312 can stitch together the first stitched traffic flow and the second stitched traffic flow to form the cross-middlebox stitched traffic flow based on the sources and destinations of the traffic flow segments with respect to the middleboxes. Further, the middlebox traffic flow stitching system 312 can stitch together the first stitched traffic flow and the second stitched traffic flow to form the cross-middlebox stitched traffic flow based on common traffic flow segments between the first stitched traffic flow and the second stitched traffic flow, e.g. as indicated by the sources and destinations of the traffic flow segments.

At step 408, the middlebox traffic flow stitching system 312 incorporates the cross-middlebox stitched traffic flow as part of network traffic data for the network environment. Specifically, the cross-middlebox stitched traffic flow can be incorporated as part of identified traffic flows in the network environment that are included as part of the network traffic data for the network environment. For example, the cross-middlebox stitched traffic flow can be stitched to traffic flows identified in a network fabric of the network environment, as part of incorporating the stitched traffic flow with network data for the network environment including the network fabric.

FIG. 5 shows an example middlebox traffic flow stitching system 500. The middlebox traffic flow stitching system 500 can function according to an applicable system for stitching together traffic flow segments through a middlebox to form a stitched traffic flow and corresponding cross-middlebox stitched traffic flows, such as the middlebox traffic flow stitching system 312 shown in FIG. 3. The middlebox traffic flow stitching system 500 can stitch together traffic flows using flow records collected or otherwise exported from a middlebox. Specifically, the middlebox traffic flow stitching system 500 can identify sources and destinations of traffic flow segments, and potentially corresponding flow directions of the segments, and subsequently stitch together traffic flows based on transaction identifiers assigned to the traffic flow segments and the sources and destinations of the traffic flow segments.

All of portions of the middlebox traffic flow stitching system 500 can be implemented at an applicable collector for collecting flow records from a middlebox, such as the middlebox traffic flow segment collectors 310 shown in FIG. 3. Additionally, all or portions of the middlebox traffic flow stitching system 500 can be implemented at a middlebox, e.g. as part of an agent. Further, all or portions of the middlebox traffic flow stitching system 500 can be implemented at an applicable system for monitoring network traffic in a network environment, such as the network traffic monitoring system 100 shown in FIG. 1.

The middlebox traffic flow stitching system 500 includes a flow records hash table maintainer 502, a flow records hash table datastore 504, a traffic flow segment stitcher 506, and a completed flow identifier 508. The flow records hash table maintainer 502 functions to maintain a flow records hash table. The flow records hash table maintainer 502 can maintain a hash table based on flow records collected from or otherwise exported by a middlebox. In maintaining a flow records hash table, the flow records hash table maintainer 502 can generate and update one or more flow records hash table stored in the flow records hash table datastore 504.

TABLE 1

T1
C -> VIP

T1
IP ->

Server

T1
Server ->

IP

T1
VIP -> C

T2
C -> VIP

Table 1, shown above, illustrates an example of a flow records hash table maintained by the flow records hash table maintainer 502 and stored in the flow records hash table datastore 504. The example flow records hash table includes a plurality of entries. Each entry corresponds to a traffic flow segment passing through a middlebox. Further, each entry includes a transaction identifier and a source and destination identifier for each traffic flow segment. For example, the first entry corresponds to a traffic flow segment passing from the client, C, to a port on the middlebox, VIP. Further in the example, the first entry includes a transaction identifier, T1, assigned to the traffic flow segment, e.g. by a middlebox. In another example, the second entry corresponds to a second traffic flow segment passing from the middlebox, IP, to a server, signified by “Server” in the entry. Flow records hash tables can include entries with different transaction identifiers corresponding to different traffic flows. Specifically, the example flow records hash table has a first entry including a first transaction identifier T1 and a fifth entry including a second transaction identifier T2.

The traffic flow segment stitcher 506 functions to stitch together traffic flow segments at a middlebox to form a stitched traffic flow corresponding to a traffic flow through the middlebox. Specifically, the traffic flow segment stitcher 506 can stitch together traffic flow segments using a flow records hash table, e.g. stored in the flow records hash table datastore 504. In using a flow records hash table to stitch together traffic flow segments, the traffic flow segment stitcher 506 can stitch together traffic flows based on transaction identifiers included as part of entries corresponding to traffic flow segments in the flow records hash table. For example, the traffic flow segment stitcher 506 can stitch together a first traffic flow segment corresponding to the first entry in the example hash table and a second traffic flow segment corresponding to the second entry in the example hash table based on both entries including the same transaction identifier T1.

Further, the traffic flow segment stitcher 506 can function to stitch together stitched traffic flows to form a cross-middlebox stitched traffic flow. Specifically, the traffic flow segment stitcher 506 can stitch together stitched traffic flows using one or more flow records hash tables, e.g. stored in the flow records hash table datastore 504. More specifically, the traffic flow segment stitcher 506 can use flow records hash tables to identify common traffic flow segments between stitched traffic flows. Subsequently, the traffic flow segment stitcher 506 can stitch together the stitched traffic flow based on the common traffic flow segments to form a cross-middlebox stitched traffic flow.

In using a flow records hash table to stitch together traffic flow segments, the traffic flow segment stitcher 506 can group entries in the hash table to form grouped entries and subsequently use the grouped entries to stitch together traffic flow segments and already stitched traffic flow segments. More specifically, the traffic flow segment stitcher 506 can group entries that share a transaction identifier to form grouped entries. For example, the traffic flow segment stitcher 506 can group the first four entries together based on the entries all having the same transaction identifier T1. Subsequently, based on entries being grouped together to form grouped entries, the traffic flow segment stitcher 506 can stitch together traffic flows corresponding to the entries in the grouped entries. For example, the traffic flow segment stitcher 506 can group the first four entries in the example flow records hash table and subsequently stitch traffic flow segments corresponding to the first four entries based on the grouping of the first four entries.

Further, in using a flow records hash table to stitch together traffic flow segments and already stitched traffic flow segments, the traffic flow segment stitcher 506 can identify flow directions and/or sources and destinations of the traffic flow segments based on corresponding entries of the traffic flow segments in the flow records hash table. More specifically, the traffic flow segment stitcher 506 can identify flow directions of traffic flow segments based on identifiers of sources and destinations of the segments in corresponding entries in a flow records hash table. For example, the traffic flow segment stitcher 506 can identify that a traffic flow segment corresponding to the first entry in the example hash table moves from a client to a middlebox based on the flow segment originating at the client and terminating at a VIP port at the middlebox, as indicated by the first entry in the table. Subsequently, using flow directions and/or sources and destinations of traffic flow segments identified from a flow records hash table, the traffic flow segment stitcher 506 can actually stitch together the traffic flow segments to form either or both stitched traffic flow and cross-middlebox stitched traffic flows. For example, the traffic flow segment stitcher 506 can stitch a traffic flow segment corresponding to the fourth entry in the example hash table after a traffic flow segment corresponding to the third entry in the example hash table.

The traffic flow segment stitcher 506 can stitch together traffic flow segments to form stitched traffic flow segments based on both directions of the traffic flow segments, as identified from corresponding entries in a flow records hash table, and transaction identifiers included in the flow records hash table. Specifically, the traffic flow segment stitcher 506 can identify to stitch together traffic flow segments with corresponding entries in a flow records hash table that share a common transaction identifier. For example, the traffic flow segment stitcher 506 can determine to stitch together traffic flow segments corresponding to the first four entries in the example flow records hash table based on the first four entries sharing the same transaction identifier T1. Additionally, the traffic flow segment stitcher 506 can determine an order to stitch together traffic flow segments based on flow directions of the traffic flow segments identified from corresponding entries of the segments in a flow records hash table. For example, the traffic flow segment stitcher 506 can determine to stitch together a third traffic flow segment corresponding to the third entry in the example hash table after a second traffic flow segment corresponding to the second entry in the example hash table based on flow directions of the segments identified from the entries.

The completed flow identifier 508 functions to identify a completed traffic flow occurring through the middlebox. A completed traffic flow can correspond to establishment of a connection between a client and a server and vice versa. For example, a completed traffic flow can include a request transmitted from a client to a middlebox, and the request transmitted from the middlebox to another middlebox before it is finally transmitted to a server. Further in the example, the completed traffic flow can include completion of the request from the client to the server through the middleboxes and completion of a response to the request from the server to the client through the middleboxes. The completed flow identifier 508 can identify a completed flow based on flow records. More specifically, the completed flow identifier 508 can identify a completed flow based on one or more flow records hash tables. For example, the completed flow identifier 508 can identify the first four entries form a completed flow based on both the first entry beginning at the client and the last entry ending at the client, and all entries having the same transaction identifier T1.

The traffic flow segment stitcher 506 can push or otherwise export traffic flow data for stitched traffic flows and corresponding cross-middlebox stitched traffic flows. More specifically, the traffic flow segment stitcher 506 can export traffic flow data for incorporation with network traffic data for a network environment. For example, the traffic flow segment stitcher 506 can export traffic flow data to the network traffic monitoring system 100, where the traffic flow data can be combined with network traffic data for a network environment. The traffic flow segment stitcher 506 can push traffic flow data based on identification of a completed traffic flow by the completed flow identifier 508. More specifically, the traffic flow segment stitcher 506 can export traffic flow data indicating a stitched traffic flow of a completed traffic flow upon identification that the traffic flow is actually a completed flow. This can ensure that data for stitched traffic flows and corresponding cross-middlebox stitched traffic flows is only pushed or otherwise provided when it is known that the stitched traffic flows correspond to completed traffic flows.

FIG. 6 depicts a diagram of an example network environment 600 for discovering configurations of middleboxes based on stitched traffic flows about the middleboxes. The example network environment 600 shown in FIG. 6 includes a first client 602-1, a second client 602-2, and a third client 602-3 (herein referred to as “clients 602”). The network environment 600 also includes a first group of servers 604-1, a second group of servers 604-2, and a third group of servers 604-3 (herein referred to as “groups of servers 604”). While only three clients 602 and three groups of servers 604 are shown, in various embodiments, the network environment 600 can include more or fewer clients and groups of servers. Additionally, while the servers are shown as groups of servers, in various embodiments, the network environment 600 can include single servers that are not grouped into groups of servers.

The groups of servers 604 function to provide network service access to the clients 602 in the network environment 600. Specifically, the groups of servers 604 can send and receive data to and from the clients 602 as part of providing the clients 602 access to network services. Servers can be grouped into the groups of servers 604 based, at least in part, on types of network services provided by the servers. For example, the first group of servers 604-1 can include a group of storage servers. Further, the groups of servers 604 can each provide different types of network services. For example, the first group of servers 604-1 can be storage servers, the second group of servers 604-2 can be database servers, and the third group of servers 604-3 can be web servers.

The groups of servers 604 communicate with the clients 602 and vice versa as part of accessing network services through a middlebox 606. While only one middlebox 606 is shown in FIG. 6, the environment 600 can include multiple middleboxes that form a chain between the group of servers 604 and the clients 602. The clients and the servers 604 can communicate through traffic flows, and corresponding traffic flow segments, through the middlebox 606. Specifically, the first client 602-1 can communicate with the first group of servers 604-1 through the first group of traffic flow segments 608-1, the second client 602-2 can communicate with the second group of servers 604-2 through the second group of traffic flow segments 608-2, and the third client 602-3 can communicate with the third group of servers 604-3 through the third group of traffic flow segments 608-3. The first, second, and third group of traffic flow segments 608-1, 608-2, and 608-3 can be jointly referred to as “the traffic flow segments 608.

The traffic flow segments 608, can be stitched together to form stitched traffic flows at the middlebox 606. An applicable system for stitching together traffic flow segments at a middlebox, such as the middlebox traffic flow stitching system 312 described herein, can stitch together the traffic flow segments 608 to form stitched traffic flows at the middlebox 606. Additionally, the environment 600 can include additional middleboxes separate from the middlebox 606, and the traffic flow segments 608 can be stitched together to form cross-middlebox stitched traffic flows across, at least in part, the middlebox 606. An applicable system for stitching together traffic flows at a middlebox, such as the middlebox traffic flow stitching system 312 described herein, can stitch together the traffic flow segments 608 to form cross-middlebox stitched traffic flows. Stitched traffic flows and cross-middlebox stitched traffic flows between the clients 602 and the groups of servers 604 can pass through the middlebox 606 and ultimately extend between the clients 602 and the groups of servers 604 to form client-server stitched traffic flows.

The environment 600 also includes a middlebox configuration discovery engine 610. The middlebox configuration discovery engine 610 functions to automatically identify configurations of middleboxes in operating to provide network service access. Configurations of a middlebox in operating to provide network service access can include which ports, e.g. VIP ports, and/or which protocols are used to communicate with a specific server. Additionally, specific servers can be associated with providing a specific network service type. Accordingly, configurations of a middlebox in operating to provide network service access can include which ports, e.g. VIP ports, and/or which protocols are used to transmit data for providing a specific type of network service. For example, a configuration of a middlebox can include which VIP ports are used to communicate with specific servers that provide web services.

In automatically identifying configurations of middleboxes operating to provide network services access, the middlebox configuration discovery engine 610 can reduce burdens on network administrators. In particular, network environments generally include a large number of intermediate nodes, e.g. middleboxes, between clients and servers. Typically, identifying a configuration of these intermediate nodes requires effort and intervention from network administrators. For example, network administrators have to gather data of servers a node is communicating with and analyze the data to determine configurations of the nodes. This can be extremely burdensome for typical network environments that include large numbers of nodes between clients and servers. Therefore, in automatically identifying configurations of middleboxes operating to provide network service access, the middlebox configuration discovery engine 610 can reduce burdens on network administrators, especially in managing network environments with large numbers of intermediate nodes, e.g. middleboxes, between clients and servers.

Further in automatically identifying configurations of middleboxes, the middlebox configuration discovery engine 610 can lead to more complete network monitoring including more accurate problem detection and troubleshooting. Specifically, as nodes between a client and a server, e.g. middleboxes, are treated as black boxes it is difficult to identify how the nodes are actually configured to operate in a network environment. As nodes are treated as black boxes with respect to traffic flows through the nodes, it is difficult to automatically identify how the nodes are actually configured to operate in a network environment. This is problematic as a node can be configured to operate according to an expected configuration but actually be operating at a different or unexpected configuration. By automatically identifying configurations of middleboxes, the middlebox configuration discovery engine 610 can facilitate more complete network monitoring including more accurate problem detection and troubleshooting, e.g. by identifying middleboxes that are not operating according to an expected configuration.

The middlebox configuration discovery engine 610 can automatically identify a configuration of a middlebox operating as a load balancer for providing access to network services. Specifically, the middlebox configuration discovery engine 610 can identify a configuration of a middlebox as it sends traffic flows to specific servers or groups of servers that actually provide the network service, as part of load balancing client access to different network services. More specifically, the middlebox configuration discovery engine 610 can identify which protocols and/or ports a middlebox uses to send service requests for specific types of services to corresponding servers capable of fulfilling the service requests. For example, the middlebox configuration discovery engine 610 can identify that a middlebox is using a specific port to send database access requests and another specific port to send storage service requests.

The middlebox configuration discovery engine 610 can identify a configuration of a middlebox based on stitched traffic flows at the middlebox. The stitched traffic flows at the middlebox used by the middlebox configuration discovery engine 610 to identify a configuration of the middlebox can be single stitched traffic flows or multiple stitched traffic flows combined together to form cross-middlebox stitched traffic flows. For example, a middlebox can be the only intermediate node between clients and servers, and single stitched traffic flows around the middlebox that extend between the clients and the servers can be used to identify a configuration of the middlebox. In another example, a middlebox can be in a chain of middleboxes between clients and servers, and the middlebox configuration discovery engine 610 can identify a configuration of the middlebox using cross-middlebox stitched traffic flows that extend across the chain of middleboxes.

In identifying a configuration of a middlebox using stitched traffic flows, the middlebox configuration discovery engine 610 can associate the stitched traffic flows with specific network service types. More specifically, the middlebox configuration discovery engine 610 can associate stitched traffic flows with types of network services accessed through the stitched traffic flows. For example, if a stitched traffic flow is used to access a web service, then the middlebox configuration discovery engine 610 can associate the stitched traffic flow with web services. In another example, if a stitched traffic flow is used to access a database service, then the middlebox configuration discovery engine 610 can associate the stitched traffic flow with database services. In yet another example, if a stitched traffic flow is used to access a storage service, then the middlebox configuration discovery engine 610 can associate the stitched traffic flow with storage services.

In associating stitched traffic flows with specific network service types, the middlebox configuration discovery engine 610 can identify the network service types to associate with the stitched traffic flows. The middlebox configuration discovery engine 610 can associate a stitched traffic flow with a service type based on an identification of a server ultimately included as either or both a destination and an origination point in the stitched traffic flow. Specifically, servers can be associated with providing specific service types, and the middlebox configuration discovery engine 610 can associate a stitched traffic flow with a service type associated with a specific server that is included in the stitched traffic flow. For example, if a middlebox sends a traffic segment, as part of a stitched traffic flow, to a server for providing web services, then the middlebox configuration discovery engine 610 can associate the stitched traffic flow with web services. The middlebox configuration discovery engine 610 can identify a network service type to associate with a stitched traffic flow based on input, e.g. user input, describing either or both services offered by a server, an identification of the server, and a port used in providing the service by the server. For example, user input can specify that a server is used in providing storage services, and the middlebox configuration discovery engine 610 can associate stitched traffic flows including traffic flow segments sent to the server with the service type of storage services.

The middlebox configuration discovery engine 610 can use service types of network services associated with stitched traffic flows to identify a configuration of middlebox using the stitched traffic flows. Specifically, the middlebox configuration discovery engine 610 can use service types associated with stitched traffic flows to identify ports, e.g. VIP ports, and/or protocols used by the middlebox configuration discovery engine 610 to send data for providing the specific network service types using the stitched traffic flow. For example, if a middlebox is using a specific protocol to send traffic in a stitched flow associated with data storage, then the middlebox configuration discovery engine 610 can identify the middlebox is using the protocol to send data for the service of data storage.

Further, the middlebox configuration discovery engine 610 can associate ports at a middlebox with network service types for purposes of identifying a configuration of the middlebox. More specifically, the middlebox configuration discovery engine 610 can correlate a specific network service type with one or more ports used to transmit traffic flow segments of a stitched traffic flow associated with providing the specific network service type. Subsequently, the middlebox configuration discovery engine 610 can identify the configuration of the middlebox based on the specific network service type correlated with the one or more ports. For example, the middlebox configuration discovery engine 610 can correlate a port with one of a web-based service, a database service, and a storage service based on traffic flow segments of stitched traffic flows passing through the port. In turn, specific services correlated with specific ports at a middlebox can indicate or otherwise be used to identify a configuration of the middlebox.

In associating ports at a middlebox with network service type, the middlebox configuration discovery engine 610 can identify specific ports that specific stitched traffic flows pass through. In particular, the middlebox configuration discovery engine 610 can identify ports at a middlebox that stitched traffic flows pass through and subsequently associate the ports with network service types associated with the ports. The middlebox configuration discovery engine 610 can identify a port that a stitched traffic flow passes through at a middlebox based on an originating port on the middlebox and a destination port on a server. More specifically, the middlebox configuration discovery engine 610 can identify a port on a middlebox that a stitched traffic flow passes through based on an originating port on the middlebox and a destination port on a server connected by a traffic flow segment in the stitched traffic flow. Conversely, the middlebox configuration discovery engine 610 can identify a port that a stitched traffic flow passes through at a middlebox based on an originating port on the middlebox and a destination on a client. More specifically, the middlebox configuration discovery engine 610 can identify a port on a middlebox that a stitched traffic flow passes through based on an originating port on the middlebox and a destination on a client connected by a traffic flow segment in the stitched traffic flow.

In automatically identifying a configuration of a middlebox operating as a load balancer, the middlebox configuration discovery engine 610 can identify one or more load balancing schemes utilized by the middlebox to perform load balancing, e.g. as part of the configuration of the middlebox. For example, the middlebox configuration discovery engine 610 can identify whether a middlebox is using a round-robin based load balancing scheme. A round-robin load balancing scheme can include that a middlebox sends traffic flows to specific different servers, e.g. in a pattern. In another example, the middlebox configuration discovery engine 610 can identify whether a middlebox is using a persistent load balancing scheme. A persistent load balancing scheme can include that a middlebox continuously sends traffic flows to the same specific servers.

The middlebox configuration discovery engine 610 can identify a load balancing scheme used by a middlebox as part of a configuration of the middlebox based on stitched traffic flows at the middlebox. Specifically, the middlebox configuration discovery engine 610 can identify whether each subsequent outbound stitched traffic flow, e.g. a stitched traffic flow including a traffic flow segment from a middlebox to a server, is destined to a different server from a previous outbound stitched traffic flow or the same server as the previous outbound stitched traffic flow. If the middlebox configuration discovery engine 610 determines that each subsequent outbound stitched traffic flow is destined to a different server, e.g. from the stitched traffic flows themselves, then the middlebox configuration discovery engine 610 can identify that a middlebox is operating according to a round-robin load balancing scheme. Conversely, if the middlebox configuration discovery engine 610 determines that each subsequent outbound stitched traffic flow is destined to the same server, then the middlebox configuration discovery engine 610 can identify that a middlebox is operating according to a persistent load balancing scheme.

In utilizing stitched traffic flows to identify a load balancing scheme used by a middlebox, the middlebox configuration discovery engine 610 can use clients at which the stitched traffic flows originate to determine the load balancing scheme. Specifically, the middlebox configuration discovery engine 610 can identify what servers receive stitched traffic flows from specific clients. Subsequently, if the middlebox configuration discovery engine 610 determines that the same servers receive traffic flows from the same clients, then the middlebox configuration discovery engine 610 can determine that a middlebox is using a persistent load balancing scheme. More specifically, if the middlebox configuration discovery engine 610 determines that the same service types are continuously accessed by the same clients through the same servers, then the middlebox configuration discovery engine 610 can determine that a middlebox is using a persistent load balancing scheme.

The middlebox configuration discovery engine 610 can validate an expected configuration of a middlebox. An expected configuration can include a desired configuration of a middlebox. Further, an expected configuration can be input by a user, e.g. a network administrator. For example, a network administrator can input that a middlebox should be implementing a round-robin load balancing scheme. In validating an expected configuration, the middlebox configuration discovery engine 610 can determine whether a middlebox is actually operating according to the expected configuration. Specifically, the middlebox configuration discovery engine 610 can compare an actual configuration of a middlebox, as determined from stitched traffic flows, with an expected configuration of the middlebox. More specifically, the middlebox configuration discovery engine 610 can determine differences between an actual configuration of a middlebox and an expected configuration of the middlebox. Subsequently, the middlebox configuration discovery engine 610 can update network data to indicate differences between an actual configuration of a middlebox and an expected configuration of the middlebox.

FIG. 7 illustrates a flowchart for an example method of automatically identifying a configuration of a middlebox using stitched traffic flow segments at the middlebox. The method shown in FIG. 7 is provided by way of example, as there are a variety of ways to carry out the method. Additionally, while the example method is illustrated with a particular order of blocks, those of ordinary skill in the art will appreciate that FIG. 7 and the blocks shown therein can be executed in any order and can include fewer or more blocks than illustrated.

Each block shown in FIG. 7 represents one or more steps, processes, methods or routines in the method. For the sake of clarity and explanation purposes, the blocks in FIG. 7 are described with reference to the network environments shown in FIGS. 3 and 6.

At step 700, the middlebox traffic flow segment collectors 310 collects flow records of traffic flow segments at a middlebox in a network environment corresponding to traffic flows passing through the middlebox. The flow records can include one or more transaction identifiers assigned to the traffic flow segments. The flow records of the traffic flow segments at the middlebox can be generated by the middlebox and subsequently exported to the middlebox traffic flow segment collectors 310. More specifically, the flow records can be exported to the middlebox traffic flow segment collectors 310 through the IPFIX protocol. The flow records can be exported to the middlebox traffic flow segment collectors 310 after each of the traffic flow segments is established, e.g. through the middlebox. Alternatively, the flow records can be exported to the middlebox traffic flow segment collectors 310 after a corresponding traffic flow of the traffic flow segments is completed, e.g. through the middlebox.

At step 702, the middlebox traffic flow stitching system 312 identifies sources and destinations of the traffic flow segments in the network environment with respect to the middlebox using the flow records. For example, the middlebox traffic flow stitching system 312 can identify whether a traffic flow segment is passing from a client to the middlebox towards a server using the flow records. In another example, the middlebox traffic flow stitching system 312 can identify whether a traffic flow segment is passing from a server to the middlebox towards a client using the flow records. The middlebox traffic flow stitching system 312 can identify flow directions of the traffic flow segments based on either or both sources and destinations of the traffic flow segments included as part of the flow records. For example, the middlebox traffic flow stitching system 312 can identify a flow direction of a traffic flow segment based on an IP address of a server where the flow segment started and a SNIP port on the middlebox that ends the flow segment at the middlebox.

At step 704, the middlebox traffic flow stitching system 312 stitches together the traffic flow segments to form a plurality of stitched traffic flows at the middlebox. More specifically, the middlebox traffic flow stitching system 312 can stitch together the traffic flow segments to form the plurality of stitched traffic flows at the middlebox based on one or more transaction identifiers assigned to the traffic flow segments. Further, the middlebox traffic flow stitching system 312 can stitch together the traffic flow segments to form the plurality of stitched traffic flows at the middlebox based on the sources and destinations of the traffic flow segments with respect to the middlebox. For example, traffic flow segments sharing the same transaction identifiers can be stitched together based on the directions of the traffic flow segments to form the plurality of stitched traffic flows, e.g. based on the flow records. More specifically, the one or more transaction identifiers assigned to the traffic flow segments can be indicated by the flow records collected at step 400 and subsequently used to stitch the traffic flow segments together to form the plurality of stitched traffic flows.

At step 706, the middlebox configuration discovery engine 610 automatically identifies a configuration of the middlebox operating in the network environment based on the stitched traffic flows at the middlebox. In determining a configuration of the middlebox based on the stitched traffic flows, the stitched traffic flows can be associated with network service types, e.g. based on the servers where the stitched traffic flows originate or end. Subsequently, the configuration of the middlebox can be identified based on the network service types associated with the stitched traffic flows. Further, in automatically identifying a configuration of the middlebox, a load balancing scheme used by the middlebox in operating as a load balancer can be determined, e.g. based on the stitched traffic flows.

The disclosure now turns to FIGS. 8 and 9, which illustrate example network devices and computing devices, such as switches, routers, load balancers, client devices, and so forth.

FIG. 8 illustrates an example network device 800 suitable for performing switching, routing, load balancing, and other networking operations. Network device 800 includes a central processing unit (CPU) 804, interfaces 802, and a bus 810 (e.g., a PCI bus). When acting under the control of appropriate software or firmware, the CPU 804 is responsible for executing packet management, error detection, and/or routing functions. The CPU 804 preferably accomplishes all these functions under the control of software including an operating system and any appropriate applications software. CPU 804 may include one or more processors 808, such as a processor from the INTEL X86 family of microprocessors. In some cases, processor 808 can be specially designed hardware for controlling the operations of network device 800. In some cases, a memory 806 (e.g., non-volatile RAM, ROM, etc.) also forms part of CPU 804. However, there are many different ways in which memory could be coupled to the system.

The interfaces 802 are typically provided as modular interface cards (sometimes referred to as “line cards”). Generally, they control the sending and receiving of data packets over the network and sometimes support other peripherals used with the network device 800. Among the interfaces that may be provided are Ethernet interfaces, frame relay interfaces, cable interfaces, DSL interfaces, token ring interfaces, and the like. In addition, various very high-speed interfaces may be provided such as fast token ring interfaces, wireless interfaces, Ethernet interfaces, Gigabit Ethernet interfaces, ATM interfaces, HSSI interfaces, POS interfaces, FDDI interfaces, WIFI interfaces, 3G/4G/5G cellular interfaces, CAN BUS, LoRA, and the like. Generally, these interfaces may include ports appropriate for communication with the appropriate media. In some cases, they may also include an independent processor and, in some instances, volatile RAM. The independent processors may control such communications intensive tasks as packet switching, media control, signal processing, crypto processing, and management. By providing separate processors for the communications intensive tasks, these interfaces allow the master microprocessor 804 to efficiently perform routing computations, network diagnostics, security functions, etc.

Although the system shown in FIG. 8 is one specific network device of the present subject matter, it is by no means the only network device architecture on which the present subject matter can be implemented. For example, an architecture having a single processor that handles communications as well as routing computations, etc., is often used. Further, other types of interfaces and media could also be used with the network device 800.

Regardless of the network device's configuration, it may employ one or more memories or memory modules (including memory 806) configured to store program instructions for the general-purpose network operations and mechanisms for roaming, route optimization and routing functions described herein. The program instructions may control the operation of an operating system and/or one or more applications, for example. The memory or memories may also be configured to store tables such as mobility binding, registration, and association tables, etc. Memory 706 could also hold various software containers and virtualized execution environments and data.

The network device 800 can also include an application-specific integrated circuit (ASIC), which can be configured to perform routing and/or switching operations. The ASIC can communicate with other components in the network device 800 via the bus 810, to exchange data and signals and coordinate various types of operations by the network device 800, such as routing, switching, and/or data storage operations, for example.

FIG. 9 illustrates a computing system architecture 900 wherein the components of the system are in electrical communication with each other using a connection 905, such as a bus. Exemplary system 900 includes a processing unit (CPU or processor) 910 and a system connection 905 that couples various system components including the system memory 915, such as read only memory (ROM) 920 and random access memory (RAM) 925, to the processor 910. The system 900 can include a cache of high-speed memory connected directly with, in close proximity to, or integrated as part of the processor 910. The system 900 can copy data from the memory 915 and/or the storage device 930 to the cache 912 for quick access by the processor 910. In this way, the cache can provide a performance boost that avoids processor 910 delays while waiting for data. These and other modules can control or be configured to control the processor 910 to perform various actions. Other system memory 915 may be available for use as well. The memory 915 can include multiple different types of memory with different performance characteristics. The processor 910 can include any general purpose processor and a hardware or software service, such as service 1932, service 2934, and service 3936 stored in storage device 930, configured to control the processor 910 as well as a special-purpose processor where software instructions are incorporated into the actual processor design. The processor 910 may be a completely self-contained computing system, containing multiple cores or processors, a bus, memory controller, cache, etc. A multi-core processor may be symmetric or asymmetric.

To enable user interaction with the system 900, an input device 945 can represent any number of input mechanisms, such as a microphone for speech, a touch-sensitive screen for gesture or graphical input, keyboard, mouse, motion input, speech and so forth. An output device 935 can also be one or more of a number of output mechanisms known to those of skill in the art. In some instances, multimodal systems can enable a user to provide multiple types of input to communicate with the system 900. The communications interface 940 can generally govern and manage the user input and system output. There is no restriction on operating on any particular hardware arrangement and therefore the basic features here may easily be substituted for improved hardware or firmware arrangements as they are developed.

Storage device 930 is a non-volatile memory and can be a hard disk or other types of computer readable media which can store data that are accessible by a computer, such as magnetic cassettes, flash memory cards, solid state memory devices, digital versatile disks, cartridges, random access memories (RAMs) 925, read only memory (ROM) 920, and hybrids thereof.

The storage device 930 can include services 932, 934, 936 for controlling the processor 910. Other hardware or software modules are contemplated. The storage device 930 can be connected to the system connection 905. In one aspect, a hardware module that performs a particular function can include the software component stored in a computer-readable medium in connection with the necessary hardware components, such as the processor 910, connection 905, output device 935, and so forth, to carry out the function.

For clarity of explanation, in some instances the present technology may be presented as including individual functional blocks including functional blocks comprising devices, device components, steps or routines in a method embodied in software, or combinations of hardware and software.

In some embodiments the computer-readable storage devices, medius, and memories can include a cable or wireless signal containing a bit stream and the like. However, when mentioned, non-transitory computer-readable storage media expressly exclude media such as energy, carrier signals, electromagnetic waves, and signals per se.

Methods according to the above-described examples can be implemented using computer-executable instructions that are stored or otherwise available from computer readable media. Such instructions can comprise, for example, instructions and data which cause or otherwise configure a general purpose computer, special purpose computer, or special purpose processing device to perform a certain function or group of functions. Portions of computer resources used can be accessible over a network. The computer executable instructions may be, for example, binaries, intermediate format instructions such as assembly language, firmware, or source code. Examples of computer-readable media that may be used to store instructions, information used, and/or information created during methods according to described examples include magnetic or optical disks, flash memory, USB devices provided with non-volatile memory, networked storage devices, and so on.

Devices implementing methods according to these disclosures can comprise hardware, firmware and/or software, and can take any of a variety of form factors. Typical examples of such form factors include laptops, smart phones, small form factor personal computers, personal digital assistants, rackmount devices, standalone devices, and so on. Functionality described herein also can be embodied in peripherals or add-in cards. Such functionality can also be implemented on a circuit board among different chips or different processes executing in a single device, by way of further example.

The instructions, media for conveying such instructions, computing resources for executing them, and other structures for supporting such computing resources are means for providing the functions described in these disclosures.

Although a variety of examples and other information was used to explain aspects within the scope of the appended claims, no limitation of the claims should be implied based on particular features or arrangements in such examples, as one of ordinary skill would be able to use these examples to derive a wide variety of implementations. Further and although some subject matter may have been described in language specific to examples of structural features and/or method steps, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to these described features or acts. For example, such functionality can be distributed differently or performed in components other than those identified herein. Rather, the described features and steps are disclosed as examples of components of systems and methods within the scope of the appended claims.

Claim language reciting “at least one of” refers to at least one of a set and indicates that one member of the set or multiple members of the set satisfy the claim. For example, claim language reciting “at least one of A and B” means A, B, or A and B.

Claims

1. A method comprising: collecting flow records of traffic flow segments of traffic passing through a middlebox in a network environment, the flow records including transaction identifiers assigned to the traffic flow segments and a corresponding port at the middlebox at which each of the traffic flow segments passes through at the middlebox;identifying whether the traffic flow segments originate at or end at the middlebox using the flow records based on the corresponding port at the middlebox at which each of the traffic flow segments passes through at the middlebox;stitching together the traffic flow segments about the middlebox, based on the ports at which each of the traffic flow segments passes through at the middlebox according to whether the traffic flow segments originate at or end at the middlebox and the transaction identifiers assigned to the traffic flow segments, to form one or more stitched traffic flows at the middlebox in the network environment; andautomatically identifying a configuration of the middlebox operating to perform operations on the traffic based on the one or more stitched traffic flows at the middlebox in the network environment as part of identifying one or more threats in the network environment through one or more machine learning techniques.
2. The method of claim 1, wherein the one or more stitched traffic flows pass through the middlebox ultimately between one or more clients and one or more servers and the one or more stitched traffic flows form at least part of one or more client-server stitched traffic flows that extend between the one or more clients and the one or more servers and are used to automatically identify the configuration of the middlebox, the configuration of the middlebox including the configuration of the middlebox in facilitating network service access to the one or more clients in the network environment through the one or more servers.
3. The method of claim 2, wherein the one or more stitched traffic flows form at least part of one or more cross-middlebox stitched traffic flows that pass through multiple middleboxes including the middlebox.
4. The method of claim 2, wherein the one or more stitched traffic flows include a plurality of stitched traffic flows at the middlebox, the method further comprising: identifying service types of network services provided by the one or more servers to the one or more clients through the network environment through corresponding stitched traffic flows of the plurality of stitched traffic flows;associating the corresponding stitched traffic flows to corresponding service types of the service types of the network services; andutilizing the service types of the network services associated with the corresponding stitched traffic flows to automatically identify the configuration of the middlebox.
5. The method of claim 4, further comprising: associating specific ports of the middlebox with one or more service types of the network services based on the service types associated with the corresponding stitched traffic flows flowing through the specific ports at the middlebox; andautomatically identifying the configuration of the middlebox based on the one or more service types of the network services associated with the specific ports.
6. The method of claim 5, wherein the one or more service types of the network services associated with the specific ports include at least one of a web-based service, a database service, and a storage service.
7. The method of claim 5, wherein whether a stitched traffic flow of the corresponding stitched traffic flows passes through a specific port of the specific ports of the middlebox is identified from the stitched traffic flow based on an originating port on the middlebox and a destination port of a server of the one or more serves included as part of a traffic flow segment of the traffic flow segments used to form the stitched traffic flow.
8. The method of claim 4, wherein the corresponding service types associated with the corresponding stitched traffic flows are identified from a specific server of the one or more servers at which the corresponding stitched traffic flows originate or begin.
9. The method of claim 1, further comprising identifying a load balancing scheme implemented by the middlebox in operating in the network environment as part of automatically identifying the configuration of the middlebox based on the stitched traffic flows at the middlebox in the network environment.
10. The method of claim 1, further comprising: determining whether each subsequent outbound stitched traffic flow at the middlebox is destined from the middlebox to a different server from each previous outbound stitched traffic flow at the middlebox; andidentifying the configuration of the middlebox as a round-robin load balancing configuration if it is determined that each subsequent outbound stitched traffic flow at the middlebox is destined from the middlebox to a different server from each previous outbound stitched traffic flow.
11. The method of claim 10, further comprising: determining whether each stitched traffic flow at the middlebox and originating from a specific client is destined to a same server; andidentifying the configuration of the middlebox as a persistent load balancing configuration if it is determined that each stitched traffic flow at the middlebox and originating from the specific client is destined to the same server.
12. The method of claim 1, further comprising incorporating the configuration of the middlebox operating in the network environment into flow data for the network environment.
13. The method of claim 1, further comprising: comparing the configuration of the middlebox with an expected configuration of the middlebox to identify differences between the configuration of the middlebox and the expected configuration of the middlebox exist; andupdating network data to indicate the differences between the configuration of the middlebox and the expected configuration of the middlebox.
14. The method of claim 1, wherein the flow records are collected from the middlebox as the middlebox exports the flow records using an Internet Protocol Flow Information Export protocol.
15. A system comprising: one or more processors; andat least one computer-readable storage medium having stored therein instructions which, when executed by the one or more processors, cause the one or more processors to perform operations comprising:collecting flow records of traffic flow segments of traffic passing through a middlebox in a network environment, the flow records including transaction identifiers assigned to the traffic flow segments and a corresponding port at the middlebox at which each of the traffic flow segments passes through at the middlebox;identifying whether the traffic flow segments originate at or end at the middlebox using the flow records based on the corresponding port at the middlebox at which each of the traffic flow segments passes through at the middlebox;stitching together the traffic flow segments about the middlebox, based on the ports at which each of the traffic flow segments passes through at the middlebox according to whether the traffic flow segments originate at or end at the middlebox and the transaction identifiers assigned to the traffic flow segments, to form a plurality of stitched traffic flows at the middlebox in the network environment, wherein the plurality of stitched traffic flows pass through the middlebox ultimately between one or more clients and one or more servers and the plurality of stitched traffic flows are client-server stitched traffic flows that extend between the one or more clients and the one or more servers; andautomatically identifying a configuration of the middlebox operating to perform operations on the traffic based on the plurality of stitched traffic flows at the middlebox in the network environment as part of identifying one or more threats in the network environment through one or more machine learning techniques.
16. The system of claim 15, wherein the instructions which, when executed by the one or more processors, further cause the one or more processors to perform operations comprising: identifying service types of network services provided by the one or more servers to the one or more clients through the network environment through corresponding stitched traffic flows of the plurality of stitched traffic flows;associating the corresponding stitched traffic flows to corresponding service types of the service types of the network services; andutilizing the service types of the network services associated with the corresponding stitched traffic flows to automatically identify the configuration of the middlebox.
17. The system of claim 16, wherein the instructions, which, when executed by the one or more processors, further cause the one or more processors to perform operations comprising: associating specific ports of the middlebox with one or more service types of the network services based on the service types associated with the corresponding stitched traffic flows flowing through the specific ports at the middlebox; andautomatically identifying the configuration of the middlebox based on the one or more service types of the network services associated with the specific ports.
18. The system of claim 17, wherein the one or more service types of the network services associated with the specific ports include at least one of a web-based service, a database service, and a storage service.
19. The system of claim 17, wherein whether a stitched traffic flow of the corresponding stitched traffic flows passes through a specific port of the specific ports of the middlebox is identified from the stitched traffic flow based on an originating port on the middlebox and a destination port of a server of the one or more serves included as part of a traffic flow segment of the traffic flow segments used to form the stitched traffic flow.
20. A non-transitory computer-readable storage medium having stored therein instructions which, when executed by a processor, cause the processor to perform operations comprising: collecting flow records of traffic flow segments of traffic passing through a middlebox in a network environment and a corresponding port at the middlebox at which each of the traffic flow segments passes through at the middlebox;identifying whether the traffic flow segments originate at or end at the middlebox using the flow records based on the corresponding port at the middlebox at which each of the traffic flow segments passes through at the middlebox;stitching together the traffic flow segments about the middlebox, based on the ports at which each of the traffic flow segments passes through at the middlebox according to whether the traffic flow segments originate at or end at the middlebox and transaction identifiers assigned to the traffic flow segments, to form one or more stitched traffic flows at the middlebox in the network environment; andautomatically identifying a configuration of the middlebox operating to perform operations on the traffic based on the one or more stitched traffic flows at the middlebox in the network environment as part of identifying one or more threats in the network environment through one or more machine learning techniques.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Patent Application No. 62/621,970, filed on Jan. 25, 2018, entitled “Automatic Configuration Discovery Based on Traffic Flow Data,” the content of which is incorporated herein by reference in its entirety.

US Referenced Citations (670)

Number	Name	Date	Kind
5086385	Launey et al.	Feb 1992	A
5319754	Meinecke et al.	Jun 1994	A
5400246	Wilson et al.	Mar 1995	A
5436909	Dev et al.	Jul 1995	A
5555416	Owens et al.	Sep 1996	A
5726644	Jednacz et al.	Mar 1998	A
5742829	Davis et al.	Apr 1998	A
5822731	Schultz	Oct 1998	A
5831848	Rielly et al.	Nov 1998	A
5903545	Sabourin et al.	May 1999	A
6012096	Link et al.	Jan 2000	A
6141595	Gloudeman et al.	Oct 2000	A
6144962	Weinberg et al.	Nov 2000	A
6239699	Ronnen	May 2001	B1
6247058	Miller et al.	Jun 2001	B1
6249241	Jordan et al.	Jun 2001	B1
6330562	Boden et al.	Dec 2001	B1
6353775	Nichols	Mar 2002	B1
6525658	Streetman et al.	Feb 2003	B2
6546420	Lemler et al.	Apr 2003	B1
6597663	Rekhter	Jul 2003	B1
6611896	Mason, Jr. et al.	Aug 2003	B1
6654750	Adams et al.	Nov 2003	B1
6728779	Griffin et al.	Apr 2004	B1
6801878	Hintz et al.	Oct 2004	B1
6816461	Scrandis et al.	Nov 2004	B1
6847993	Novaes et al.	Jan 2005	B1
6848106	Hipp	Jan 2005	B1
6925490	Novaes et al.	Aug 2005	B1
6958998	Shorey	Oct 2005	B2
6983323	Cantrell et al.	Jan 2006	B2
6996817	Birum et al.	Feb 2006	B2
6999452	Drummond-Murray et al.	Feb 2006	B1
7002464	Bruemmer et al.	Feb 2006	B2
7024468	Meyer et al.	Apr 2006	B1
7096368	Kouznetsov et al.	Aug 2006	B2
7111055	Falkner	Sep 2006	B2
7120934	Ishikawa	Oct 2006	B2
7133923	MeLampy et al.	Nov 2006	B2
7162643	Sankaran et al.	Jan 2007	B1
7181769	Keanini et al.	Feb 2007	B1
7185103	Jain	Feb 2007	B1
7203740	Putzolu et al.	Apr 2007	B1
7302487	Ylonen et al.	Nov 2007	B2
7337206	Wen et al.	Feb 2008	B1
7349761	Cruse	Mar 2008	B1
7353511	Ziese	Apr 2008	B1
7356679	Le et al.	Apr 2008	B1
7360072	Soltis et al.	Apr 2008	B1
7370092	Aderton et al.	May 2008	B2
7395195	Suenbuel et al.	Jul 2008	B2
7444404	Wetherall et al.	Oct 2008	B2
7457870	Lownsbrough	Nov 2008	B1
7466681	Ashwood-Smith et al.	Dec 2008	B2
7467205	Dempster et al.	Dec 2008	B1
7496040	Seo	Feb 2009	B2
7496575	Buccella et al.	Feb 2009	B2
7530105	Gilbert et al.	May 2009	B2
7539770	Meier	May 2009	B2
7543052	Cesa Klein	Jun 2009	B1
7568107	Rathi et al.	Jul 2009	B1
7610330	Quinn et al.	Oct 2009	B1
7633942	Bearden et al.	Dec 2009	B2
7644438	Dash et al.	Jan 2010	B1
7676570	Levy et al.	Mar 2010	B2
7681131	Quarterman et al.	Mar 2010	B1
7693947	Judge et al.	Apr 2010	B2
7743242	Oberhaus et al.	Jun 2010	B2
7752307	Takara	Jul 2010	B2
7774498	Kraemer et al.	Aug 2010	B1
7783457	Cunningham	Aug 2010	B2
7787480	Mehta et al.	Aug 2010	B1
7788477	Huang et al.	Aug 2010	B1
7808897	Mehta et al.	Oct 2010	B1
7813822	Hoffberg	Oct 2010	B1
7844696	Labovitz et al.	Nov 2010	B2
7844744	Abercrombie et al.	Nov 2010	B2
7864707	Dimitropoulos et al.	Jan 2011	B2
7873025	Patel et al.	Jan 2011	B2
7873074	Boland	Jan 2011	B1
7874001	Beck et al.	Jan 2011	B2
7885197	Metzler	Feb 2011	B2
7895649	Brook et al.	Feb 2011	B1
7904420	Ianni	Mar 2011	B2
7930752	Hertzog et al.	Apr 2011	B2
7934248	Yehuda et al.	Apr 2011	B1
7957934	Greifeneder	Jun 2011	B2
7961637	McBeath	Jun 2011	B2
7970946	Djabarov et al.	Jun 2011	B1
7975035	Popescu et al.	Jul 2011	B2
8001610	Chickering Roger et al.	Aug 2011	B1
8005935	Pradhan et al.	Aug 2011	B2
8040232	Oh et al.	Oct 2011	B2
8040822	Proulx et al.	Oct 2011	B2
8056134	Ogilvie	Nov 2011	B1
8115617	Thubert et al.	Feb 2012	B2
8135657	Kapoor et al.	Mar 2012	B2
8156430	Newman	Apr 2012	B2
8160063	Maltz et al.	Apr 2012	B2
8179809	Eppstein et al.	May 2012	B1
8181248	Oh et al.	May 2012	B2
8185824	Mitchell et al.	May 2012	B1
8239365	Salman	Aug 2012	B2
8239915	Satish et al.	Aug 2012	B1
8250657	Nachenberg et al.	Aug 2012	B1
8255972	Azagury et al.	Aug 2012	B2
8266697	Coffman	Sep 2012	B2
8272875	Jurmain	Sep 2012	B1
8281397	Vaidyanathan et al.	Oct 2012	B2
8291495	Burns et al.	Oct 2012	B1
8296847	Mendonca et al.	Oct 2012	B2
8311973	Zadeh	Nov 2012	B1
8365286	Poston	Jan 2013	B2
8370407	Devarajan et al.	Feb 2013	B1
8381289	Pereira et al.	Feb 2013	B1
8391270	Van Der Stok et al.	Mar 2013	B2
8407164	Malik et al.	Mar 2013	B2
8407798	Lotem et al.	Mar 2013	B1
8413235	Chen et al.	Apr 2013	B1
8442073	Skubacz et al.	May 2013	B2
8451731	Lee et al.	May 2013	B1
8462212	Kundu et al.	Jun 2013	B1
8489765	Vasseur et al.	Jul 2013	B2
8499348	Rubin	Jul 2013	B1
8516590	Ranadive et al.	Aug 2013	B1
8527977	Cheng et al.	Sep 2013	B1
8549635	Muttik et al.	Oct 2013	B2
8570861	Brandwine et al.	Oct 2013	B1
8572600	Chung et al.	Oct 2013	B2
8572734	McConnell et al.	Oct 2013	B2
8572735	Ghosh et al.	Oct 2013	B2
8572739	Cruz et al.	Oct 2013	B1
8588081	Salam et al.	Nov 2013	B2
8600726	Varshney et al.	Dec 2013	B1
8613084	Dalcher	Dec 2013	B2
8615803	Dacier et al.	Dec 2013	B2
8630316	Haba	Jan 2014	B2
8631464	Belakhdar et al.	Jan 2014	B2
8640086	Bonev et al.	Jan 2014	B2
8656493	Capalik	Feb 2014	B2
8661544	Yen et al.	Feb 2014	B2
8677487	Balupari et al.	Mar 2014	B2
8683389	Bar-Yam et al.	Mar 2014	B1
8706914	Duchesneau	Apr 2014	B2
8713676	Pandrangi et al.	Apr 2014	B2
8719452	Ding et al.	May 2014	B1
8719835	Kanso et al.	May 2014	B2
8750287	Bui et al.	Jun 2014	B2
8752042	Ratica	Jun 2014	B2
8752179	Zaitsev	Jun 2014	B2
8755396	Sindhu et al.	Jun 2014	B2
8762951	Kosche et al.	Jun 2014	B1
8769084	Westerfeld et al.	Jul 2014	B2
8775577	Alford et al.	Jul 2014	B1
8776180	Kumar et al.	Jul 2014	B2
8812448	Anderson et al.	Aug 2014	B1
8812725	Kulkarni	Aug 2014	B2
8813236	Saha et al.	Aug 2014	B1
8825848	Dotan et al.	Sep 2014	B1
8832013	Adams et al.	Sep 2014	B1
8832461	Saroiu et al.	Sep 2014	B2
8849926	Marzencki et al.	Sep 2014	B2
8881258	Paul et al.	Nov 2014	B2
8887238	Howard et al.	Nov 2014	B2
8904520	Nachenberg et al.	Dec 2014	B1
8908685	Patel et al.	Dec 2014	B2
8914497	Xiao et al.	Dec 2014	B1
8931043	Cooper et al.	Jan 2015	B2
8954610	Berke et al.	Feb 2015	B2
8955124	Kim et al.	Feb 2015	B2
8966021	Allen	Feb 2015	B1
8966625	Zuk et al.	Feb 2015	B1
8973147	Pearcy et al.	Mar 2015	B2
8984331	Quinn	Mar 2015	B2
8990386	He et al.	Mar 2015	B2
8996695	Anderson et al.	Mar 2015	B2
8997227	Mhatre et al.	Mar 2015	B1
9014047	Alcala et al.	Apr 2015	B2
9015716	Fletcher et al.	Apr 2015	B2
9071575	Lemaster et al.	Jun 2015	B2
9088598	Zhang et al.	Jul 2015	B1
9110905	Polley et al.	Aug 2015	B2
9117075	Yeh	Aug 2015	B1
9130836	Kapadia et al.	Sep 2015	B2
9152789	Natarajan et al.	Oct 2015	B2
9160764	Stiansen et al.	Oct 2015	B2
9170917	Kumar et al.	Oct 2015	B2
9178906	Chen et al.	Nov 2015	B1
9185127	Neou et al.	Nov 2015	B2
9191400	Ptasinski et al.	Nov 2015	B1
9191402	Yan	Nov 2015	B2
9197654	Ben-Shalom et al.	Nov 2015	B2
9225793	Dutta et al.	Dec 2015	B2
9237111	Banavalikar et al.	Jan 2016	B2
9246702	Sharma et al.	Jan 2016	B1
9246773	Degioanni	Jan 2016	B2
9253042	Lumezanu et al.	Feb 2016	B2
9253206	Fleischman	Feb 2016	B1
9258217	Duffield et al.	Feb 2016	B2
9281940	Matsuda et al.	Mar 2016	B2
9286047	Avramov et al.	Mar 2016	B1
9294486	Chiang et al.	Mar 2016	B1
9317574	Brisebois et al.	Apr 2016	B1
9319384	Yan et al.	Apr 2016	B2
9369435	Short et al.	Jun 2016	B2
9369479	Lin	Jun 2016	B2
9378068	Anantharam et al.	Jun 2016	B2
9396327	Shimomura Tsuyoshi et al.	Jun 2016	B2
9405903	Xie et al.	Aug 2016	B1
9417985	Baars et al.	Aug 2016	B2
9418222	Rivera et al.	Aug 2016	B1
9426068	Dunbar et al.	Aug 2016	B2
9454324	Madhavapeddi	Sep 2016	B1
9462013	Boss et al.	Oct 2016	B1
9465696	McNeil et al.	Oct 2016	B2
9501744	Brisebois et al.	Nov 2016	B1
9531589	Clemm et al.	Dec 2016	B2
9563517	Natanzon et al.	Feb 2017	B1
9621413	Lee	Apr 2017	B1
9634915	Bley	Apr 2017	B2
9645892	Patwardhan	May 2017	B1
9684453	Holt et al.	Jun 2017	B2
9697033	Koponen et al.	Jul 2017	B2
9733973	Prasad et al.	Aug 2017	B2
9749145	Banavalikar et al.	Aug 2017	B2
9800608	Korsunsky et al.	Oct 2017	B2
9904584	Konig et al.	Feb 2018	B2
9916538	Zadeh et al.	Mar 2018	B2
9935851	Gandham et al.	Apr 2018	B2
10009240	Rao et al.	Jun 2018	B2
20010028646	Arts et al.	Oct 2001	A1
20020053033	Cooper et al.	May 2002	A1
20020097687	Meiri et al.	Jul 2002	A1
20020103793	Koller et al.	Aug 2002	A1
20020107857	Teraslinna	Aug 2002	A1
20020141343	Bays	Oct 2002	A1
20020184393	Leddy et al.	Dec 2002	A1
20030023601	Fortier, Jr. et al.	Jan 2003	A1
20030065986	Fraenkel et al.	Apr 2003	A1
20030097439	Strayer et al.	May 2003	A1
20030126242	Chang	Jul 2003	A1
20030145232	Poletto et al.	Jul 2003	A1
20030151513	Herrmann et al.	Aug 2003	A1
20030154399	Zuk et al.	Aug 2003	A1
20030177208	Harvey, IV	Sep 2003	A1
20040019676	Iwatsuki et al.	Jan 2004	A1
20040030776	Cantrell et al.	Feb 2004	A1
20040213221	Civanlar et al.	Oct 2004	A1
20040220984	Dudfield et al.	Nov 2004	A1
20040243533	Dempster et al.	Dec 2004	A1
20040255050	Takehiro et al.	Dec 2004	A1
20040268149	Aaron	Dec 2004	A1
20050028154	Smith et al.	Feb 2005	A1
20050039104	Shah et al.	Feb 2005	A1
20050063377	Bryant et al.	Mar 2005	A1
20050083933	Fine et al.	Apr 2005	A1
20050108331	Osterman	May 2005	A1
20050122325	Twait	Jun 2005	A1
20050138157	Jung et al.	Jun 2005	A1
20050166066	Ahuja et al.	Jul 2005	A1
20050177829	Vishwanath	Aug 2005	A1
20050182681	Bruskotter et al.	Aug 2005	A1
20050185621	Sivakumar et al.	Aug 2005	A1
20050198247	Perry et al.	Sep 2005	A1
20050198371	Smith et al.	Sep 2005	A1
20050198629	Vishwanath	Sep 2005	A1
20050207376	Ashwood-Smith et al.	Sep 2005	A1
20050257244	Joly et al.	Nov 2005	A1
20050289244	Sahu et al.	Dec 2005	A1
20060048218	Lingafelt et al.	Mar 2006	A1
20060077909	Saleh et al.	Apr 2006	A1
20060080733	Khosmood et al.	Apr 2006	A1
20060089985	Poletto	Apr 2006	A1
20060095968	Portolani et al.	May 2006	A1
20060143432	Rothman et al.	Jun 2006	A1
20060156408	Himberger et al.	Jul 2006	A1
20060159032	Ukrainetz et al.	Jul 2006	A1
20060173912	Lindvall et al.	Aug 2006	A1
20060195448	Newport	Aug 2006	A1
20060272018	Fouant	Nov 2006	A1
20060274659	Ouderkirk	Dec 2006	A1
20060280179	Meier	Dec 2006	A1
20060285495	Aitken	Dec 2006	A1
20060294219	Ogawa et al.	Dec 2006	A1
20070014275	Bettink et al.	Jan 2007	A1
20070025306	Cox et al.	Feb 2007	A1
20070044147	Choi et al.	Feb 2007	A1
20070097976	Wood et al.	May 2007	A1
20070118654	Jamkhedkar et al.	May 2007	A1
20070127491	Verzijp et al.	Jun 2007	A1
20070162420	Ou et al.	Jul 2007	A1
20070169179	Narad	Jul 2007	A1
20070195729	Li et al.	Aug 2007	A1
20070195794	Fujita et al.	Aug 2007	A1
20070195797	Patel et al.	Aug 2007	A1
20070201474	Isobe	Aug 2007	A1
20070211637	Mitchell	Sep 2007	A1
20070214348	Danielsen	Sep 2007	A1
20070230415	Malik	Oct 2007	A1
20070232265	Park et al.	Oct 2007	A1
20070250930	Aziz et al.	Oct 2007	A1
20070300061	Kim et al.	Dec 2007	A1
20080002697	Anantharamaiah et al.	Jan 2008	A1
20080022385	Crowell et al.	Jan 2008	A1
20080028389	Genty et al.	Jan 2008	A1
20080046708	Fitzgerald et al.	Feb 2008	A1
20080049633	Edwards et al.	Feb 2008	A1
20080056124	Nanda et al.	Mar 2008	A1
20080082662	Danliker Richard et al.	Apr 2008	A1
20080101234	Nakil et al.	May 2008	A1
20080120350	Grabowski et al.	May 2008	A1
20080126534	Mueller et al.	May 2008	A1
20080141246	Kuck et al.	Jun 2008	A1
20080155245	Lipscombe et al.	Jun 2008	A1
20080250122	Zsigmond et al.	Oct 2008	A1
20080270199	Chess et al.	Oct 2008	A1
20080282347	Dadhia et al.	Nov 2008	A1
20080295163	Kang	Nov 2008	A1
20080301765	Nicol et al.	Dec 2008	A1
20090059934	Aggarwal et al.	Mar 2009	A1
20090064332	Porras et al.	Mar 2009	A1
20090109849	Wood et al.	Apr 2009	A1
20090133126	Jang et al.	May 2009	A1
20090138590	Lee et al.	May 2009	A1
20090180393	Nakamura	Jul 2009	A1
20090241170	Kumar et al.	Sep 2009	A1
20090292795	Ford et al.	Nov 2009	A1
20090296593	Prescott	Dec 2009	A1
20090300180	Dehaan et al.	Dec 2009	A1
20090307753	Dupont et al.	Dec 2009	A1
20090313373	Hanna et al.	Dec 2009	A1
20090313698	Wahl	Dec 2009	A1
20090319912	Serr et al.	Dec 2009	A1
20090323543	Shimakura	Dec 2009	A1
20090328219	Narayanaswamy	Dec 2009	A1
20100005288	Rao et al.	Jan 2010	A1
20100049839	Parker et al.	Feb 2010	A1
20100054241	Shah et al.	Mar 2010	A1
20100077445	Schneider et al.	Mar 2010	A1
20100095293	O'Neill et al.	Apr 2010	A1
20100095367	Narayanaswamy	Apr 2010	A1
20100095377	Krywaniuk	Apr 2010	A1
20100138526	DeHaan et al.	Jun 2010	A1
20100138810	Komatsu et al.	Jun 2010	A1
20100148940	Gelvin et al.	Jun 2010	A1
20100153316	Duffield et al.	Jun 2010	A1
20100153696	Beachem et al.	Jun 2010	A1
20100180016	Bugwadia et al.	Jul 2010	A1
20100194741	Finocchio	Aug 2010	A1
20100220584	DeHaan et al.	Sep 2010	A1
20100235514	Beachem	Sep 2010	A1
20100235879	Burnside et al.	Sep 2010	A1
20100235915	Memon et al.	Sep 2010	A1
20100287266	Asati et al.	Nov 2010	A1
20100303240	Beachem	Dec 2010	A1
20100306180	Johnson et al.	Dec 2010	A1
20100317420	Hoffberg	Dec 2010	A1
20100319060	Aiken et al.	Dec 2010	A1
20110004935	Moffie et al.	Jan 2011	A1
20110010585	Bugenhagen et al.	Jan 2011	A1
20110022641	Werth et al.	Jan 2011	A1
20110055381	Narasimhan et al.	Mar 2011	A1
20110055388	Yumerefendi et al.	Mar 2011	A1
20110066719	Miryanov et al.	Mar 2011	A1
20110069685	Tofighbakhsh	Mar 2011	A1
20110072119	Bronstein et al.	Mar 2011	A1
20110083125	Komatsu et al.	Apr 2011	A1
20110085556	Breslin et al.	Apr 2011	A1
20110103259	Aybay et al.	May 2011	A1
20110107074	Chan et al.	May 2011	A1
20110107331	Evans et al.	May 2011	A1
20110126136	Abella et al.	May 2011	A1
20110126275	Anderson et al.	May 2011	A1
20110145885	Rivers et al.	Jun 2011	A1
20110153039	Gvelesiani et al.	Jun 2011	A1
20110153811	Jeong et al.	Jun 2011	A1
20110158088	Lofstrand et al.	Jun 2011	A1
20110170860	Smith et al.	Jul 2011	A1
20110173490	Narayanaswamy et al.	Jul 2011	A1
20110185423	Sallam	Jul 2011	A1
20110196957	Ayachitula et al.	Aug 2011	A1
20110202655	Sharma et al.	Aug 2011	A1
20110214174	Herzog et al.	Sep 2011	A1
20110225207	Subramanian et al.	Sep 2011	A1
20110228696	Agarwal et al.	Sep 2011	A1
20110238793	Bedare et al.	Sep 2011	A1
20110246663	Melsen et al.	Oct 2011	A1
20110277034	Hanson	Nov 2011	A1
20110283277	Castillo et al.	Nov 2011	A1
20110302652	Westerfeld	Dec 2011	A1
20110314148	Petersen et al.	Dec 2011	A1
20110317982	Xu et al.	Dec 2011	A1
20120005542	Petersen et al.	Jan 2012	A1
20120051219	Hart	Mar 2012	A1
20120079592	Pandrangi	Mar 2012	A1
20120089664	Igelka	Apr 2012	A1
20120102361	Sass et al.	Apr 2012	A1
20120102543	Kohli et al.	Apr 2012	A1
20120110188	Van Biljon et al.	May 2012	A1
20120117226	Tanaka et al.	May 2012	A1
20120117642	Lin et al.	May 2012	A1
20120136996	Seo et al.	May 2012	A1
20120137278	Draper et al.	May 2012	A1
20120137361	Yi et al.	May 2012	A1
20120140626	Anand et al.	Jun 2012	A1
20120195198	Regan	Aug 2012	A1
20120197856	Banka et al.	Aug 2012	A1
20120198541	Reeves	Aug 2012	A1
20120216271	Cooper et al.	Aug 2012	A1
20120218989	Tanabe et al.	Aug 2012	A1
20120219004	Balus et al.	Aug 2012	A1
20120233348	Winters	Sep 2012	A1
20120233473	Vasseur et al.	Sep 2012	A1
20120240232	Azuma	Sep 2012	A1
20120246303	Petersen et al.	Sep 2012	A1
20120254109	Shukla et al.	Oct 2012	A1
20120260227	Shukla et al.	Oct 2012	A1
20120278021	Lin et al.	Nov 2012	A1
20120281700	Koganti et al.	Nov 2012	A1
20120300628	Prescott et al.	Nov 2012	A1
20130003538	Greenburg et al.	Jan 2013	A1
20130003733	Venkatesan et al.	Jan 2013	A1
20130006935	Grisby	Jan 2013	A1
20130007435	Bayani	Jan 2013	A1
20130038358	Cook et al.	Feb 2013	A1
20130041934	Annamalaisami et al.	Feb 2013	A1
20130054682	Malik et al.	Feb 2013	A1
20130085889	Fitting et al.	Apr 2013	A1
20130086272	Chen et al.	Apr 2013	A1
20130103827	Dunlap et al.	Apr 2013	A1
20130107709	Campbell et al.	May 2013	A1
20130124807	Nielsen et al.	May 2013	A1
20130125107	Bandakka et al.	May 2013	A1
20130145099	Liu et al.	Jun 2013	A1
20130148663	Xiong	Jun 2013	A1
20130159999	Chiueh et al.	Jun 2013	A1
20130173784	Wang et al.	Jul 2013	A1
20130174256	Powers	Jul 2013	A1
20130179487	Lubetzky et al.	Jul 2013	A1
20130179879	Zhang et al.	Jul 2013	A1
20130198517	Mazzarella	Aug 2013	A1
20130198839	Wei et al.	Aug 2013	A1
20130201986	Sajassi et al.	Aug 2013	A1
20130205293	Levijarvi et al.	Aug 2013	A1
20130219161	Fontignie et al.	Aug 2013	A1
20130219500	Lukas et al.	Aug 2013	A1
20130232498	Mangtani et al.	Sep 2013	A1
20130242999	Kamble et al.	Sep 2013	A1
20130246925	Ahuja et al.	Sep 2013	A1
20130247201	Alperovitch et al.	Sep 2013	A1
20130254879	Chesla et al.	Sep 2013	A1
20130268994	Cooper et al.	Oct 2013	A1
20130275579	Hernandez et al.	Oct 2013	A1
20130283374	Zisapel et al.	Oct 2013	A1
20130290521	Labovitz	Oct 2013	A1
20130297771	Osterloh et al.	Nov 2013	A1
20130301472	Allan	Nov 2013	A1
20130304900	Trabelsi et al.	Nov 2013	A1
20130305369	Karta et al.	Nov 2013	A1
20130318357	Abraham et al.	Nov 2013	A1
20130326623	Kruglick	Dec 2013	A1
20130333029	Chesla et al.	Dec 2013	A1
20130336164	Yang et al.	Dec 2013	A1
20130346736	Cook et al.	Dec 2013	A1
20130347103	Veteikis et al.	Dec 2013	A1
20140006610	Formby et al.	Jan 2014	A1
20140006871	Lakshmanan et al.	Jan 2014	A1
20140012814	Bercovici et al.	Jan 2014	A1
20140019972	Yahalom et al.	Jan 2014	A1
20140031005	Sumcad et al.	Jan 2014	A1
20140033193	Palaniappan	Jan 2014	A1
20140036688	Stassinopoulos et al.	Feb 2014	A1
20140040343	Nickolov et al.	Feb 2014	A1
20140047185	Peterson et al.	Feb 2014	A1
20140047372	Gnezdov et al.	Feb 2014	A1
20140056318	Hansson et al.	Feb 2014	A1
20140059200	Nguyen et al.	Feb 2014	A1
20140074946	Dirstine et al.	Mar 2014	A1
20140089494	Dasari et al.	Mar 2014	A1
20140092884	Murphy et al.	Apr 2014	A1
20140096058	Molesky et al.	Apr 2014	A1
20140105029	Jain et al.	Apr 2014	A1
20140115219	Ajanovic et al.	Apr 2014	A1
20140129942	Rathod	May 2014	A1
20140136680	Joshi	May 2014	A1
20140137109	Sharma et al.	May 2014	A1
20140140244	Kapadia et al.	May 2014	A1
20140143825	Behrendt et al.	May 2014	A1
20140149490	Luxenberg et al.	May 2014	A1
20140156814	Barabash et al.	Jun 2014	A1
20140156861	Cruz-Aguilar et al.	Jun 2014	A1
20140164607	Bai et al.	Jun 2014	A1
20140165200	Singla	Jun 2014	A1
20140165207	Engel et al.	Jun 2014	A1
20140173623	Chang et al.	Jun 2014	A1
20140192639	Smirnov	Jul 2014	A1
20140201717	Mascaro et al.	Jul 2014	A1
20140215573	Cepuran	Jul 2014	A1
20140215621	Xaypanya et al.	Jul 2014	A1
20140224784	Kohler	Aug 2014	A1
20140225603	Auguste et al.	Aug 2014	A1
20140233387	Zheng et al.	Aug 2014	A1
20140269777	Rothstein et al.	Sep 2014	A1
20140280499	Basavaiah et al.	Sep 2014	A1
20140281030	Cui et al.	Sep 2014	A1
20140286354	Van De Poel et al.	Sep 2014	A1
20140289854	Mahvi	Sep 2014	A1
20140298461	Hohndel et al.	Oct 2014	A1
20140307686	Su et al.	Oct 2014	A1
20140317278	Kersch et al.	Oct 2014	A1
20140317737	Shin et al.	Oct 2014	A1
20140325649	Zhang	Oct 2014	A1
20140330616	Lyras	Nov 2014	A1
20140331048	Casas-Sanchez et al.	Nov 2014	A1
20140331276	Frascadore et al.	Nov 2014	A1
20140331280	Porras et al.	Nov 2014	A1
20140331304	Wong	Nov 2014	A1
20140348182	Chandra et al.	Nov 2014	A1
20140351203	Kunnatur et al.	Nov 2014	A1
20140351415	Harrigan et al.	Nov 2014	A1
20140359695	Chari et al.	Dec 2014	A1
20150006689	Szilagyi et al.	Jan 2015	A1
20150006714	Jain	Jan 2015	A1
20150009840	Pruthi et al.	Jan 2015	A1
20150026809	Altman et al.	Jan 2015	A1
20150033305	Shear et al.	Jan 2015	A1
20150036480	Huang et al.	Feb 2015	A1
20150036533	Sodhi et al.	Feb 2015	A1
20150039751	Harrigan et al.	Feb 2015	A1
20150046882	Menyhart et al.	Feb 2015	A1
20150052441	Degioanni	Feb 2015	A1
20150058976	Carney et al.	Feb 2015	A1
20150067143	Babakhan et al.	Mar 2015	A1
20150067786	Fiske	Mar 2015	A1
20150081762	Mason	Mar 2015	A1
20150082151	Liang et al.	Mar 2015	A1
20150082430	Sridhara et al.	Mar 2015	A1
20150085665	Kompella et al.	Mar 2015	A1
20150095332	Beisiegel et al.	Apr 2015	A1
20150112933	Satapathy	Apr 2015	A1
20150113133	Srinivas et al.	Apr 2015	A1
20150124608	Agarwal et al.	May 2015	A1
20150124652	Dhamapurikar et al.	May 2015	A1
20150128133	Pohlmann	May 2015	A1
20150128205	Mahaffey et al.	May 2015	A1
20150138993	Forster et al.	May 2015	A1
20150142962	Srinivas et al.	May 2015	A1
20150195291	Zuk et al.	Jul 2015	A1
20150222939	Gallant et al.	Aug 2015	A1
20150249622	Phillips et al.	Sep 2015	A1
20150256555	Choi et al.	Sep 2015	A1
20150261842	Huang et al.	Sep 2015	A1
20150261886	Wu et al.	Sep 2015	A1
20150271008	Jain et al.	Sep 2015	A1
20150271255	Mackay et al.	Sep 2015	A1
20150295945	Canzanese, Jr. et al.	Oct 2015	A1
20150312233	Graham, III et al.	Oct 2015	A1
20150347554	Vasantham et al.	Dec 2015	A1
20150356297	Yang et al.	Dec 2015	A1
20150358352	Chasin et al.	Dec 2015	A1
20160006753	McDaid et al.	Jan 2016	A1
20160019030	Shukla et al.	Jan 2016	A1
20160020959	Rahaman	Jan 2016	A1
20160021131	Heilig	Jan 2016	A1
20160026552	Holden et al.	Jan 2016	A1
20160036636	Erickson et al.	Feb 2016	A1
20160036837	Jain et al.	Feb 2016	A1
20160050132	Zhang et al.	Feb 2016	A1
20160072815	Rieke et al.	Mar 2016	A1
20160080414	Kolton et al.	Mar 2016	A1
20160087861	Kuan et al.	Mar 2016	A1
20160094394	Sharma et al.	Mar 2016	A1
20160094529	Mityagin	Mar 2016	A1
20160103692	Guntaka et al.	Apr 2016	A1
20160105350	Greifeneder et al.	Apr 2016	A1
20160112270	Danait et al.	Apr 2016	A1
20160112284	Pon et al.	Apr 2016	A1
20160119234	Valencia Lopez et al.	Apr 2016	A1
20160127395	Underwood et al.	May 2016	A1
20160147585	Konig et al.	May 2016	A1
20160162308	Chen et al.	Jun 2016	A1
20160162312	Doherty et al.	Jun 2016	A1
20160173446	Nantel	Jun 2016	A1
20160173535	Barabash et al.	Jun 2016	A1
20160183093	Vaughn et al.	Jun 2016	A1
20160191476	Schutz Harald et al.	Jun 2016	A1
20160205002	Rieke et al.	Jul 2016	A1
20160216994	Sefidcon et al.	Jul 2016	A1
20160217022	Velipasaoglu et al.	Jul 2016	A1
20160255082	Rathod	Sep 2016	A1
20160269424	Chandola et al.	Sep 2016	A1
20160269442	Shieh	Sep 2016	A1
20160269482	Jamjoom et al.	Sep 2016	A1
20160294691	Joshi	Oct 2016	A1
20160308908	Kirby et al.	Oct 2016	A1
20160337204	Dubey et al.	Nov 2016	A1
20160357424	Pang et al.	Dec 2016	A1
20160357546	Chang et al.	Dec 2016	A1
20160357587	Yadav et al.	Dec 2016	A1
20160357957	Deen et al.	Dec 2016	A1
20160359592	Kulshreshtha et al.	Dec 2016	A1
20160359628	Singh et al.	Dec 2016	A1
20160359658	Yadav et al.	Dec 2016	A1
20160359673	Gupta et al.	Dec 2016	A1
20160359677	Kulshreshtha et al.	Dec 2016	A1
20160359678	Madani et al.	Dec 2016	A1
20160359679	Parasdehgheibi et al.	Dec 2016	A1
20160359680	Parasdehgheibi et al.	Dec 2016	A1
20160359686	Parasdehgheibi et al.	Dec 2016	A1
20160359695	Yadav et al.	Dec 2016	A1
20160359696	Yadav et al.	Dec 2016	A1
20160359697	Scheib et al.	Dec 2016	A1
20160359698	Deen et al.	Dec 2016	A1
20160359699	Gandham et al.	Dec 2016	A1
20160359700	Pang et al.	Dec 2016	A1
20160359701	Pang et al.	Dec 2016	A1
20160359703	Gandham et al.	Dec 2016	A1
20160359704	Gandham et al.	Dec 2016	A1
20160359705	Parasdehgheibi et al.	Dec 2016	A1
20160359708	Gandham et al.	Dec 2016	A1
20160359709	Deen et al.	Dec 2016	A1
20160359711	Deen et al.	Dec 2016	A1
20160359712	Alizadeh Attar et al.	Dec 2016	A1
20160359740	Parasdehgheibi et al.	Dec 2016	A1
20160359759	Singh et al.	Dec 2016	A1
20160359872	Yadav	Dec 2016	A1
20160359877	Kulshreshtha et al.	Dec 2016	A1
20160359878	Prasad et al.	Dec 2016	A1
20160359879	Deen et al.	Dec 2016	A1
20160359880	Pang et al.	Dec 2016	A1
20160359881	Yadav et al.	Dec 2016	A1
20160359888	Gupta et al.	Dec 2016	A1
20160359889	Yadav et al.	Dec 2016	A1
20160359890	Deen et al.	Dec 2016	A1
20160359891	Pang et al.	Dec 2016	A1
20160359897	Yadav et al.	Dec 2016	A1
20160359905	Touboul et al.	Dec 2016	A1
20160359912	Gupta et al.	Dec 2016	A1
20160359913	Gupta et al.	Dec 2016	A1
20160359914	Deen et al.	Dec 2016	A1
20160359915	Gupta et al.	Dec 2016	A1
20160359917	Rao et al.	Dec 2016	A1
20160373481	Sultan et al.	Dec 2016	A1
20160380865	Dubai et al.	Dec 2016	A1
20170006141	Bhadra	Jan 2017	A1
20170024453	Raja et al.	Jan 2017	A1
20170032310	Mimnaugh	Feb 2017	A1
20170034018	Parasdehgheibi et al.	Feb 2017	A1
20170048121	Hobbs et al.	Feb 2017	A1
20170048146	Sane	Feb 2017	A1
20170070582	Desai et al.	Mar 2017	A1
20170085483	Mihaly et al.	Mar 2017	A1
20170208487	Ratakonda et al.	Jul 2017	A1
20170237760	Holeman	Aug 2017	A1
20170250880	Akens et al.	Aug 2017	A1
20170250951	Wang et al.	Aug 2017	A1
20170289067	Lu et al.	Oct 2017	A1
20170295141	Thubert et al.	Oct 2017	A1
20170302691	Singh et al.	Oct 2017	A1
20170331747	Singh et al.	Nov 2017	A1
20170346736	Chander et al.	Nov 2017	A1
20170364380	Frye, Jr. et al.	Dec 2017	A1
20170373933	Subramanian	Dec 2017	A1
20170373972	Kaplan	Dec 2017	A1
20180006911	Dickey	Jan 2018	A1
20180007115	Nedeltchev et al.	Jan 2018	A1
20180013670	Kapadia et al.	Jan 2018	A1
20180145906	Yadav et al.	May 2018	A1
20190075056	Lu	Mar 2019	A1
20190158368	Cociglio	May 2019	A1

Foreign Referenced Citations (25)

Number	Date	Country
101093452	Dec 2007	CN
101770551	Jul 2010	CN
102521537	Jun 2012	CN
103023970	Apr 2013	CN
103716137	Apr 2014	CN
104065518	Sep 2014	CN
107196807	Sep 2017	CN
0811942	Dec 1997	EP
1076848	Jul 2002	EP
1383261	Jan 2004	EP
1450511	Aug 2004	EP
2045974	Apr 2008	EP
2043320	Apr 2009	EP
2860912	Apr 2015	EP
2887595	Jun 2015	EP
2009-016906	Jan 2009	JP
1394338	May 2014	KR
WO 2007014314	Feb 2007	WO
WO 2007070711	Jun 2007	WO
WO 2008069439	Jun 2008	WO
WO 2013030830	Mar 2013	WO
WO 2015042171	Mar 2015	WO
WO 2015099778	Jul 2015	WO
WO 2016004075	Jan 2016	WO
WO 2016019523	Feb 2016	WO

Non-Patent Literature Citations (100)

Entry
Joseph et al., “Modeling Middleboxes,” IEEE Network, Sep./Oct. 2008, pp. 20-25.
Al-Fuqaha, Ala, et al., “Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications,” IEEE Communication Surveys & Tutorials. vol. 17, No. 4, Nov. 18, 2015, pp. 2347-2376.
Baek, Kwang-Hyun, et al., “Preventing Theft of Quality of Service on Open Platforms,” 2005 Workshop of the 1st International Conference on Security and Privacy for Emerging Areas in Communication Networks, 2005, 12 pages.
Brocade Communications Systems, Inc., “Chapter 5—Configuring Virtual LANs (VLANs),” Jun. 2009, 38 pages.
Cisco Systems, Inc. “Cisco, Nexus 3000 Series NX-OS Release Notes, Release 5.0(3)U3(1),” Feb. 29, 2012, Part No. OL-26631-01, 16 pages.
Cisco Systems, Inc., “Cisco—VPN Client User Guide for Windows,” Release 4.6, Aug. 2004, 148 pages.
Cisco Systems, Inc., “Cisco 4710 Application Control Engine Appliance Hardware Installation Guide,” Nov. 2007, 66 pages.
Cisco Systems, Inc., “Cisco Data Center Network Architecture and Solutions Overview,” Feb. 2006, 19 pages.
Cisco Systems, Inc., “Cisco IOS Configuration Fundamentals Configuration Guide: Using Autoinstall and Setup,”Release 12.2, first published Apr. 2001, last updated Sep. 2003, 32 pages.
Cisco Systems, Inc., “Cisco VN-Link: Virtualization-Aware Networking,” White Paper, Mar. 2009, 10 pages.
Cisco Systems, Inc., “Cisco, Nexus 5000 Series and Cisco Nexus 2000 Series Release Notes, Cisco NX-OS Release 5.1(3)N2(1b), NX-OS Release 5.1(3)N2(1a) and NX-OS Release 5.1(3)N2(1),” Sep. 5, 2012, Part No. OL-26652-03 CO, 24 pages.
Cisco Systems, Inc., “Nexus 3000 Series NX-OS Fundamentals Configuration Guide, Release 5.0(3)U3(1): Using PowerOn Auto Provisioning,” Feb. 29, 2012, Part No. OL-26544-01, 10 pages.
Cisco Systems, Inc., “Quick Start Guide, Cisco ACE 4700 Series Application Control Engine Appliance,” Software Ve740rsion A5(1.0), Sep. 2011, 138 pages.
Cisco Systems, Inc., “Routing and Bridging Guide, Cisco ACE Application Control Engine,” Software Version A5(1.0), Sep. 2011, 248 pages.
Cisco Systems, Inc., “VMWare and Cisco Virtualization Solution: Scale Virtual Machine Networking,” Jul. 2009, 4 pages.
Cisco Systems, Inc., “Cisco Remote Integrated Service Engine for Citrix NetScaler Appliances and Cisco Nexus 7000 Series Switches Configuration Guide,” Last modified Apr. 29, 2014, 78 pages.
Cisco Technology, Inc., “Cisco IOS Software Release 12.4T Features and Hardware Support,” Feb. 2009, 174 pages.
Cisco Systems, Inc., “Cisco Application Control Engine (ACE) Troubleshooting Guide—Understanding the ACE Module Architecture and Traffic Flow,” Mar. 11, 2011, 6 pages.
Costa, Raul, et al., “An Intelligent Alarm Management System for Large-Scale Telecommunication Companies,” In Portuguese Conference on Artificial Intelligence, Oct. 2009, 14 pages.
De Carvalho, Tiago Filipe Rodrigues, “Root Cause Analysis in Large and Complex Networks,” Dec. 2008, Repositorio.ul.pt, pp. 1-55.
Foundation for Intelligent Physical Agents, “FIPA Agent Message Transport Service Specification,” Dec. 3, 2002, http://www.fipa.org; 15 pages.
Gia, Tuan Nguyen, et al., “Fog Computing in Healthcare Internet of Things: A Case Study on ECG Feature Extraction,” 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing, Oct. 26, 2015, pp. 356-363.
Kent, S., et al. “Security Architecture for the Internet Protocol,” Network Working Group, Nov. 1998, 67 pages.
Online Collins English Dictionary, 1 page (Year: 2018).
Theodorakopoulos, George, et al., “On Trust Models and Trust Evaluation Metrics for Ad Hoc Networks,” IEEE Journal on Selected Areas in Communications. vol. 24, Issue 2, Feb. 2006, pp. 318-328.
Voris, Jonathan, et al., “Bait and Snitch: Defending Computer Systems with Decoys,” Columbia University Libraries, Department of Computer Science, 2013, pp. 1-25.
Zeng, Sai, et al., “Managing Risk in Multi-node Automation of Endpoint Management,” 2014 IEEE Network Operations and Management Symposium (NOMS), 2014, 6 pages.
Arista Networks, Inc., “Application Visibility and Network Telemtry using Splunk,” Arista White Paper, Nov. 2013, 11 pages.
Australian Government Department of Defence, Intelligence and Security, “Top 4 Strategies to Mitigate Targeted Cyber Intrusions,” Cyber Security Operations Centre Jul. 2013, http://www.asd.gov.au/infosec/top-mitigations/top-4-strategies-explained.htm.
Author Unknown, “Blacklists & Dynamic Reputation: Understanding Why the Evolving Threat Eludes Blacklists,” www.dambala.com, 9 pages, Dambala, Atlanta, GA, USA.
Aydin, Galip, et al., “Architecture and Implementation of a Scalable Sensor Data Storage and Analysis Using Cloud Computing and Big Data Technologies,” Journal of Sensors, vol. 2015, Article ID 834217, Feb. 2015, 11 pages.
Backes, Michael, et al., “Data Lineage in Malicious Environments,” IEEE 2015, pp. 1-13.
Bauch, Petr, “Reader's Report of Master's Thesis, Analysis and Testing of Distributed NoSQL Datastore Riak,” May 28, 2015, Brno. 2 pages.
Bayati, Mohsen, et al., “Message-Passing Algorithms for Sparse Network Alignment,” Mar. 2013, 31 pages.
Berezinski, Przemyslaw, et al., “An Entropy-Based Network Anomaly Detection Method,” Entropy, 2015, vol. 17, www.mdpi.com/journal/entropy, pp. 2367-2408.
Berthier, Robin, et al. “Nfsight: Netflow-based Network Awareness Tool,” 2010, 16 pages.
Bhuyan, Dhiraj, “Fighting Bots and Botnets,” 2006, pp. 23-28.
Blair, Dana, et al., U.S. Appl. No. 62/106,006, filed Jan. 21, 2015, entitled “Monitoring Network Policy Compliance.”
Bosch, Greg, “Virtualization,” 2010, 33 pages.
Breen, Christopher, “MAC 911, How to dismiss Mac App Store Notifications,” Macworld.com, Mar. 24, 2014, 3 pages.
Chandran, Midhun, et al., “Monitoring in a Virtualized Environment,” GSTF International Journal on Computing, vol. 1, No. 1, Aug. 2010.
Chari, Suresh, et al., “Ensuring continuous compliance through reconciling policy with usage,” Proceedings of the 18th ACM symposium on Access control models and technologies (SACMAT '13). ACM, New York, NY, USA, 49-60.
Chen, Xu, et al., “Automating network application dependency discovery: experiences, limitations, and new solutions,” 8th USENIX conference on Operating systems design and implementation (OSDI'08), USENIX Association, Berkeley, CA, USA, 117-130.
Chou, C.W., et al., “Optical Clocks and Relativity,” Science vol. 329, Sep. 24, 2010, pp. 1630-1633.
Cisco Systems, “Cisco Network Analysis Modules (NAM) Tutorial,” Cisco Systems, Inc., Version 3.5.
Cisco Systems, Inc., “Addressing Compliance from One Infrastructure: Cisco Unified Compliance Solution Framework,” 2014.
Cisco Systems, Inc., “Cisco Application Dependency Mapping Service,” 2009.
Cisco Systems, Inc., “White Paper—New Cisco Technologies Help Customers Achieve Regulatory Compliance,” 1992-2008.
Cisco Systems, Inc., “A Cisco Guide to Defending Against Distributed Denial of Service Attacks,” May 3, 2016, 34 pages.
Cisco Systems, Inc., “Cisco Application Visibility and Control,” Oct. 2011, 2 pages.
Cisco Systems, Inc., “Cisco Tetration Platform Data Sheet”, Updated Mar. 5, 2018, 21 pages.
Cisco Technology, Inc., “Cisco Lock-and-Key:Dynamic Access Lists,” http://www/cisco.com/c/en/us/support/docs/security-vpn/lock-key/7604-13.html; Updated Jul. 12, 2006, 16 pages.
Di Lorenzo, Guisy, et al., “EXSED: An Intelligent Tool for Exploration of Social Events Dynamics from Augmented Trajectories,” Mobile Data Management (MDM), pp. 323-330, Jun. 3-6, 2013.
Duan, Yiheng, et al., Detective: Automatically Identify and Analyze Malware Processes in Forensic Scenarios via DLLs, IEEE ICC 2015—Next Generation Networking Symposium, pp. 5691-5696.
Feinstein, Laura, et al., “Statistical Approaches to DDoS Attack Detection and Response,” Proceedings of the DARPA Information Survivability Conference and Exposition (DISCEX '03), Apr. 2003, 12 pages.
George, Ashley, et al., “NetPal: A Dynamic Network Administration Knowledge Base,” 2008, pp. 1-14.
Goldsteen, Abigail, et al., “A Tool for Monitoring and Maintaining System Trustworthiness at Run Time,” REFSQ (2015), pp. 142-147.
Hamadi, S., et al., “Fast Path Acceleration for Open vSwitch in Overlay Networks,” Global Information Infastructure and Networking Symposium (GIIS), Montreal, QC, pp. 1-5, Sep. 15-19, 2014.
Heckman, Sarah, et al., “On Establishing a Benchmark for Evaluating Static Analysis Alert Prioritization and Classification Techniques,” IEEE, 2008; 10 pages.
Hewlett-Packard, “Effective use of reputation intelligence in a security operations center,” Jul. 2013, 6 pages.
Hideshima, Yusuke, et al., “STARMINE: A Visualization System for Cyber Attacks,” https://www.researchgate.net/publication/221536306, Feb. 2006, 9 pages.
Huang, Hing-Jie, et al., “Clock Skew Based Node Identification in Wireless Sensor Networks,” IEEE, 2008, 5 pages.
InternetPerils, Inc., “Control Your Internet Business Risk,” 2003-2015, https://www.internetperils.com.
Ives, Herbert, E., et al., “An Experimental Study of the Rate of a Moving Atomic Clock,” Journal of the Optical Society of America, vol. 28, No. 7, Jul. 1938, pp. 215-226.
Janoff, Christian, et al., “Cisco Compliance Solution for Hipaa Security Rule Design and Implementation Guide,” Cisco Systems, Inc., Updated Nov. 14, 2015, part 1 of 2, 350 pages.
Janoff, Christian, et al., “Cisco Compliance Solution for Hipaa Security Rule Design and Implementation Guide,” Cisco Systems, Inc., Updated Nov. 14, 2015, part 2 of 2, 588 pages.
Kerrison, Adam, et al., “Four Steps to Faster, Better Application Dependency Mapping—Laying the Foundation for Effective Business Service Models,” BMCSoftware, 2011.
Kim, Myung-Sup, et al. “A Flow-based Method for Abnormal Network Traffic Detection,” IEEE, 2004, pp. 599-612.
Kraemer, Brian, “Get to know your data center with CMDB,” TechTarget, Apr. 5, 2006, http://searchdatacenter.techtarget.com/news/118820/Get-to-know-your-data-center-with-CMDB.
Lab SKU, “VMware Hands-on Labs—HOL-SDC-1301” Version: 20140321-160709, 2013; http://docs.hol.vmware.com/HOL-2013/holsdc-1301_html_en/ (part 1 of 2).
Lab SKU, “VMware Hands-on Labs—HOL-SDC-1301” Version: 20140321-160709, 2013; http://docs.hol.vmware.com/HOL-2013/holsdc-1301_html_en/ (part 2 of 2).
Lachance, Michael, “Dirty Little Secrets of Application Dependency Mapping,” Dec. 26, 2007.
Landman, Yoav, et al., “Dependency Analyzer,” Feb. 14, 2008, http://jfrog.com/confluence/display/DA/Home.
Lee, Sihyung, “Reducing Complexity of Large-Scale Network Configuration Management,” Ph.D. Dissertation, Carniege Mellon University, 2010.
Li, Ang, et al., “Fast Anomaly Detection for Large Data Centers,” Global Telecommunications Conference (GLOBECOM 2010, Dec. 2010, 6 pages.
Li, Bingbong, et al, “A Supervised Machine Learning Approach to Classify Host Roles on Line Using sFlow,” in Proceedings of the first edition workshop on High performance and programmable networking, 2013, ACM, New York, NY, USA, 53-60.
Liu, Ting, et al., “Impala: A Middleware System for Managing Autonomic, Parallel Sensor Systems,” in Proceedings of the Ninth ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming(PPoPP '03), ACM, New York, NY, USA, Jun. 11-13, 2003, pp. 107-118.
Lu, Zhonghai, et al., “Cluster-based Simulated Annealing for Mapping Cores onto 2D Mesh Networks on Chip,” Design and Diagnostics of Electronic Circuits and Systems, pp. 1, 6, 16-18, Apr. 2008.
Matteson, Ryan, “Depmap: Dependency Mapping of Applications Using Operating System Events: a Thesis,” Master's Thesis, California Polytechnic State University, Dec. 2010.
Natarajan, Arun, et al., “NSDMiner: Automated Discovery of Network Service Dependencies,” Institute of Electrical and Electronics Engineers Infocom, Feb. 2012, 9 pages.
Navaz, A.S. Syed, et al., “Entropy based Anomaly Detection System to Prevent DDoS Attacks in Cloud,” International Journal of computer Applications (0975-8887), vol. 62, No. 15, Jan. 2013, pp. 42-47.
Neverfail, “Neverfail IT Continuity Architect,” 2015, https://web.archive.org/web/20150908090456/http://www.neverfailgroup.com/products/it-continuity-architect.
Nilsson, Dennis K., et al., “Key Management and Secure Software Updates in Wireless Process Control Environments,” In Proceedings of the First ACM Conference on Wireless Network Security (WiSec '08), ACM, New York, NY, USA, Mar. 31-Apr. 2, 2008, pp. 100-108.
Nunnally, Troy, et al., “P3D: A Parallel 3D Coordinate Visualization for Advanced Network Scans,” IEEE 2013, Jun. 9-13, 2013, 6 pages.
O'Donnell, Glenn, et al., “The CMDB Imperative: How to Realize the Dream and Avoid the Nightmares,” Prentice Hall, Feb. 19, 2009.
Ohta, Kohei, et al., “Detection, Defense, and Tracking of Internet-Wide Illegal Access in a Distributed Manner,” 2000, pp. 1-16.
Pathway Systems International Inc., “How Blueprints does Integration,” Apr. 15, 2014, 9 pages, http://pathwaysystems.com/company-blog/.
Pathway Systems International Inc., “What is Blueprints?” 2010-2016, http://pathwaysystems.com/blueprints-about/.
Popa, Lucian, et al., “Macroscope: End-Point Approach to Networked Application Dependency Discovery,” CoNEXT'09, Dec. 1-4, 2009, Rome, Italy, 12 pages.
Prasad, K. Munivara, et al., “An Efficient Detection of Flooding Attacks to Internet Threat Monitors (ITM) using Entropy Variations under Low Traffic,” Computing Communication & Networking Technologies (ICCCNT '12), Jul. 26-28, 2012, 11 pages.
Sachan, Mrinmaya, et al., “Solving Electrical Networks to incorporate Supervision in Random Walks,” May 13-17, 2013, pp. 109-110.
Sammarco, Matteo, et al., “Trace Selection for Improved WLAN Monitoring,” Aug. 16, 2013, pp. 9-14.
Shneiderman, Ben, et al., “Network Visualization by Semantic Substrates,” Visualization and Computer Graphics, vol. 12, No. 5, pp. 733,740, Sep.-Oct. 2006.
Thomas, R., “Bogon Dotted Decimal List,” Version 7.0, Team Cymru NOC, Apr. 27, 2012, 5 pages.
Wang, Ru, et al., “Learning directed acyclic graphs via bootstarp aggregating,” 2014, 47 pages, http://arxiv.org/abs/1406.2098.
Wang, Yongjun, et al., “A Network Gene-Based Framework for Detecting Advanced Persistent Threats,” Nov. 2014, 7 pages.
Witze, Alexandra, “Special relativity aces time trial, ‘Time dilation’ predicted by Einstein confirmed by lithium ion experiment,” Nature, Sep. 19, 2014, 3 pages.
Woodberg, Brad, “Snippet from Juniper SRX Series” Jun. 17, 2013, 1 page, O'Reilly Media, Inc.
Zatrochova, Zuzana, “Analysis and Testing of Distributed NoSQL Datastore Riak,” Spring, 2015, 76 pages.
Zhang, Yue, et al., “CANTINA: A Content-Based Approach to Detecting Phishing Web Sites,” May 8-12, 2007, pp. 639-648.

Related Publications (1)

	Number	Date	Country
	20190229995 A1	Jul 2019	US

Provisional Applications (1)

	Number	Date	Country
	62621970	Jan 2018	US

Automatic configuration discovery based on traffic flow data

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