Patent Grant
10728119
The subject matter of this disclosure relates in general to the field of computer networks, and more specifically for discovering applications and their interdependencies within a computer network.
An enterprise application is a set of workloads (e.g., computing, networking, and storage) that are generally distributed across various nodes (or endpoints) of a network and the relationships (e.g., connectivity, dependencies, network and security policies, etc.) between the workloads. A typical application may include a presentation tier, an application tier, and a data tier. The presentation tier may depend on the application tier and authentication services, and the application tier may depend on the web tier and external network services (e.g., a travel reservation system, an ordering tool, a billing tool, etc.). These tiers may further depend on firewall, load balancing, wide area network (WAN) acceleration, and other network services. An enterprise can include hundreds or thousands of applications of similar and different architectures.
An expansive or thorough understanding of a data center and applications running in the data center can be critical for network management tasks such as anomaly detection (e.g., network attacks and misconfiguration), asset management (e.g., monitoring, capacity planning, consolidation, migration, and continuity planning), and compliance (e.g. conformance with governmental regulations, industry standards, and corporate policies). Despite the complexities of the interrelationships among workloads discussed above, the traditional approaches for developing insight into an enterprise's workloads require comprehensive knowledge on the part of human operators and processes that are manual and largely customized for a particular enterprise. To the extent automated solutions exist, conventional techniques for application dependency mapping (ADM) suffer from various drawbacks.
Conventional automated ADM techniques often assume stationary locations for workloads, which is an invalid assumption for many networks. For example, many enterprises implement virtualization platforms that support migration of VMs and containers, such as for optimizing distribution of workloads or for failover handling. Further, hybrid cloud computing, in which an enterprise application offloads some or all of its workload to a public cloud, for instance, has become an increasingly popular solution for data centers reaching the limits of their capacity. Conventional ADM processes also ordinarily rely on static and structured feature vectors or signatures (i.e., bases of comparison) for discovering applications and application dependency maps. In addition, conventional ADM systems are often incapable of or fail to gather all relevant information for identifying workloads and their interdependencies.
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 that are illustrated in the appended drawings. Understanding that these drawings depict only 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:
The detailed description set forth below is intended as a description of various configurations of embodiments and is not intended to represent the only configurations in which the subject matter of this disclosure can be practiced. The appended drawings are incorporated herein and constitute a part of the detailed description. The detailed description includes specific details for the purpose of providing a more thorough understanding of the subject matter of this disclosure. However, it will be clear and apparent that the subject matter of this disclosure is not limited to the specific details set forth herein and may be practiced without these details. In some instances, structures and components are shown in block diagram form in order to avoid obscuring the concepts of the subject matter of this disclosure.
Overview
A network can be configured to automate discovery of applications running in the network and map the applications' interdependencies. For example, the network can determine whether nodes or endpoints (e.g., physical servers, VMs, or containers) form a cluster making up a tier or component of an application. The network can monitor network information, process information, and other relevant information of traffic passing through the network using a sensor network that provides multiple perspectives for the traffic. The sensor network can include sensors for networking devices (e.g., routers, switches, network appliances), physical servers, hypervisors or shared kernels, and virtual partitions (e.g., VMs or containers), and other network elements. The network can analyze the network information, process information, and other pertinent information to determine respective feature vectors for nodes. A feature vector may represent a fusion of the features corresponding to the network information and the features corresponding to the process information of an endpoint. The network can compare the similarity measures of the respective feature vectors of the nodes and determine each node's cluster based on the similarity measures.
Description
Various embodiments of the disclosure are discussed in detail below. While specific implementations are discussed, it should be understood that this is done for illustrative purposes only. A person skilled in the relevant art will recognize that other components and configurations may be used without departing from the spirit and scope of the disclosure.
Systems and approaches in accordance with various embodiments may overcome the foregoing and other limitations with conventional techniques for application dependency mapping by collecting, from multiple perspectives, a more thorough data set for discovering applications and analyzing the applications' dependencies, such as whether a set of nodes form a cluster of a tier or a component of an application; capturing traffic and process data in real time or substantially real time to accurately identify endpoints that make up the application even when the endpoints migrate from host to host due to virtualization and/or cloud computing; and applying machine learning (ML) to the collected data to evaluate and determine optimal features across multiple domains for discovering application dependencies and clusters.
Conventional automated application dependency mapping (ADM) solutions are traditionally limited to packet data captured by 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 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.
Conventional ADM approaches may also be limited to networks with stationary nodes. As discussed, this is unworkable in modern data centers which may migrate virtual partitions (e.g., VMs or containers) from host to host both internally (i.e., within the data center) and externally (i.e., to and from a public cloud) in the event of a network or server failure or to balance workloads in the data center. In some embodiments, a data center captures network traffic data and associated host, process, and/or user data in real time or substantially real time and analyzes the captured network data to determine an up-to-date application topology.
An integral task of application dependency mapping is clustering or identifying 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. Clustering can be prone to error if the input data is limited to only one domain or one aspect of a node's behavior (e.g., networking). Collecting additional data from other domains may provide better understanding of the node's behavior and greater accuracy in defining clusters. However, evaluating data across different domains can be a difficult endeavor.
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 may 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 may 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,
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 may be an instance of a VM, container, sandbox, or other isolated software environment. The software environment may include an operating system and application software. For software running within a virtual partition, the virtual partition may 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 may 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 may 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 108 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 may continuously send network traffic data to the collectors 108. 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 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 108. 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 may 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 may 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 may 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 may not 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 may 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 may 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 may 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 may capture data for a first packet of a flow but may 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 may 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 may 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 may 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 may be retained for a lengthier period of time for another set of flows identified as anomalous or as an attack.
Computer networks may be exposed to a variety of different attacks that expose vulnerabilities of computer systems in order to compromise their security. Some network traffic may be associated with malicious programs or devices. The analytics engine 110 may 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 may 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 may be used to dynamically update models for identifying malicious traffic patterns.
In some embodiments, the analytics engine 110 may 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 may 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 may be used. Unsupervised anomaly detection techniques may 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 may 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 may correspond to a first endpoint (e.g., network device, physical server, virtual partition, etc.) of the network, and the destination address may 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 may 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 may 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 may 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 may further include a search engine 156. The search engine 156 may be configured, for example to perform a structured search, an NLP (Natural Language Processing) search, or a visual search. Data may 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 may 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 may 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 may 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 may 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 116 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 may be too voluminous for a user to navigate. The presentation module 116 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 application programming interface (API) for the programming language Java®, which defines how a client may 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 STEM 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
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 may 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 data center 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
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 may be further interconnected by an intermediate network node, such as a router, to extend the effective size of each network. The endpoints 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 may 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 may 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 may include any number of physical servers hosting any number of virtual machines, containers, or other virtual partitions. Hosts may 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
As shown in
The data pipeline 300 also includes an ADM input data stage 304 in which a network or security administrator or other authorized user may configure an ADM run by selecting the date range of the flow data and associated data to analyze, and those nodes for which the administrator wants application dependency maps and/or cluster information. In some embodiments, the administrator can also input side information, such as server load balance, route tags, and previously identified clusters during the ADM input data stage 304. In other embodiments, the side information can be automatically pulled or another network element can push the side information for the ADM run.
The next stage of the data pipeline 300 is pre-processing 306. During the pre-processing stage 306, nodes of the network are partitioned into selected node and dependency node subnets. Selected nodes are those nodes for which the user requests application dependency maps and cluster information. Dependency nodes are those nodes that are not explicitly selected by the users for an ADM run but are nodes that communicate with the selected nodes. To obtain the partitioning information, edges of an application dependency map (i.e., flow data) and unprocessed feature vectors can be analyzed.
Other tasks can also be performed during the pre-processing stage 306, including identifying dependencies of the selected nodes and the dependency nodes; replacing the dependency nodes with tags based on the dependency nodes' subnet names; extracting feature vectors for the selected nodes, such as by aggregating daily vectors across multiple days, calculating term frequency-inverse document frequency (tf-idf), and normalizing the vectors (e.g., l2 normalization); and identifying existing clusters.
In some embodiments, the pre-processing stage 306 can include early feature fusion pre-processing. Early fusion is a fusion scheme in which features are combined into a single representation. Features may be derived from various domains (e.g., network, host, virtual partition, process, user, etc.), and a feature vector in an early fusion system may represent the concatenation of disparate feature types or domains.
Early fusion may be effective for features that are similar or have a similar structure (e.g., fields of TCP and UDP packets or flows). Such features may be characterized as being a same type or being within a same domain. Early fusion may be less effective for distant features or features of different types or domains (e.g., flow-based features versus process-based features). Thus, in some embodiments, only features in the network domain (i.e., network traffic-based features, such as packet header information, number of packets for a flow, number of bytes for a flow, and similar data) may be analyzed. In other embodiments, an ADM run may limit analysis to features in the process domain (i.e., process-based features, such as process name, parent process, process owner, etc.). In yet other embodiments, feature sets in other domains (e.g., the host domain, virtual partition domain, user domain, etc.) may be the focus of the ADM run.
After pre-processing, the data pipeline 300 may proceed to a clustering stage 308. In the clustering stage 308, various machine learning techniques can be implemented to analyze feature vectors within a single domain or across different domains to determine the optimal clustering given a set of input nodes. Machine learning is an area of computer science in which the goal is to develop models using example observations (i.e., training data), that can be used to make predictions on new observations. The models or logic are not based on theory but are empirically based or data-driven.
Machine learning can be categorized as supervised or unsupervised. In supervised learning, the training data examples contain labels for the outcome variable of interest. There are example inputs and the values of the outcome variable of interest are known in the training data. The goal of supervised learning is to learn a method for mapping inputs to the outcome of interest. The supervised models then make predictions about the values of the outcome variable for new observations. Supervised learning methods include boosting, neural networks, and random forests, among others.
Boosting is a machine learning algorithm which finds a highly accurate hypothesis (e.g., low error rate) from a combination of many “weak” hypotheses (e.g., substantial error rate). Given a data set comprising examples within a class and not within the class and weights based on the difficulty of classifying an example and a weak set of classifiers, boosting generates and calls a new weak classifier in each of a series of rounds. For each call, the distribution of weights is updated that indicates the importance of examples in the data set for the classification. On each round, the weights of each incorrectly classified example are increased, and the weights of each correctly classified example is decreased so the new classifier focuses on the difficult examples (i.e., those examples have not been correctly classified).
Neural networks are inspired by biological neural networks and consist of an interconnected group of functions or classifiers that process information using a connectionist approach. Neural networks change their structure during training, such as by merging overlapping detections within one network and training an arbitration network to combine the results from different networks. Examples of neural network-based approaches include the multilayer neural network, the auto associative neural network, the probabilistic decision-based neural network (PDBNN), and the sparse network of winnows (SNOW).
A random forest is a machine learning algorithm that relies on a combination of decision trees in which each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. A random forest can be trained for some number of trees ‘T’ by sampling ‘N’ cases of the training data at random with replacement to create a subset of the training data. At each node, a number ‘m’ of the features are selected at random from the set of all features. The feature that provides the best split is used to do a binary split on that node. At the next node, another number ‘m’ of the features are selected at random and the process is repeated.
In unsupervised learning, there are example inputs, however, no outcome values. The goal of unsupervised learning can be to find patterns in the data or predict a desired outcome. Unsupervised learning methods include principle component analysis (PCA), expectation-maximization (EM), and clustering, among others.
PCA is a machine learning algorithm that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. The number of principal components is less than or equal to the number of original variables. This transformation is defined in such a way that the first principal component has the largest possible variance (i.e., the principal component accounts for as much of the variability in the data as possible), and each succeeding component in turn has the highest variance possible under the constraint that it is orthogonal to the preceding components. The resulting vectors are an uncorrelated orthogonal basis set.
Clustering is a process that groups a set of objects into the same group (i.e., cluster) when the objects are more similar, less distant, denser, or otherwise share more attributes with respect to each other than to those in other groups. An example of clustering is the k-means algorithm in which a number of n nodes are partitioned into k clusters such that each node belongs to the cluster with the nearest mean. The algorithm proceeds by alternating steps, assignment and update. During assignment, each node is assigned to a cluster whose mean yields the least within-cluster sum of squares (WCSS) (i.e., the nearest mean). During update, the new means is calculated to be the centroids of the nodes in the new clusters. Convergence is achieved when the assignments no longer change. The k-means algorithm is an example of partition clustering. Other approaches for clustering include hierarchical clustering (e.g., agglomerative and divisive methods), density-based clustering (e.g., EM or DBSCAN), model-based clustering (e.g., decision trees or neural networks), grid-based clustering (e.g., fuzzy or evolutionary methods), among other categories.
EM is an iterative process for finding the maximum likelihood or maximum a posteriori estimates of parameters in a statistical model, where the model depends on unobserved latent variables. The EM iteration alternates between performing an expectation (E) step, which creates a function for the expectation of the log-likelihood evaluated using the current estimate for the parameters, and a maximization (M) step, which computes parameters maximizing the expected log-likelihood found during the E step. These parameter-estimates are then used to determine the distribution of the latent variables in the next E step.
During the clustering stage 308, respective feature vectors of nodes are evaluated using machine learning to identify an optimal clustering for a selected set of nodes. Supervised or unsupervised learning techniques can be used depending on the availability of training data and other related information (e.g., network topology). For example, an ADM module (or other suitable system) can receive configuration information regarding a network from a configuration management system (CMS), configuration management database (CMDB), or other similar system. In some embodiments, the ADM module can receive the configuration data in a proprietary or open source format utilized by the CMS or CMDB and translate the information to training data observations for the particular machine learning approach(es) implemented by the ADM module. In other embodiments, the CMS or CMDB and the ADM module may be closely integrated and the CMS or CMDB can automatically provide the ADM module with suitable training data. In yet other embodiments, a network administrator or authorized user may receive the configuration data from the CM and the administrator or user can manually label nodes to create the training data.
In some embodiments, the clustering stage 308 can utilize early fusion for determining clusters for a selected set of nodes.
The feature vectors can be received by an early fusion module 420 for combining the feature vectors into a monolithic feature vector. For example, feature vectors for the fusion system 400 may be encoded as bytes (i.e., structured), and the early fusion module 420 can write values of the flow vectors 410 to a first set of bytes of a byte representation of the single vector, values of the process vectors 412 to a second set of bytes of the byte representation, and values of the host vectors 414 to a third set of bytes of the byte representation. In some embodiments, feature vectors for the system 400 may be encoded as variable-length strings (i.e., unstructured), and the early fusion module 420 can concatenate strings corresponding to the flow vectors 410, the process vectors 412, and the host vectors 414, using delimiters to mark the bounds of each feature across the flow, process, and host domains. The early fusion module 420 may also perform other tasks relating to combining features from multiple domains into a single representation, such as encoding/decoding feature vectors, truncating feature vectors and/or feature vector values, handling missing feature values, among other tasks.
The monolithic feature vectors generated by the early fusion module 420 may be received by first clustering module 430, which can implement a suitable clustering algorithm (e.g., k-means, hierarchical clustering, EM, DBScan, decision trees, neural networks, fuzzy clustering, evolutionary clustering, etc.). The suitable clustering algorithm can be user-specified or can be based on a maximum rule (e.g., the ML algorithm with the highest level of confidence, highest level of accuracy, etc.), a minimum rule (e.g., the ML algorithm with the lowest error rate, least memory utilization, least CPU utilization, least network utilization, etc.), a majority rule (e.g., the greatest number of ADM runs meeting a specified threshold level of accuracy), or some combination thereof.
In some embodiments, the particular machine learning algorithm implemented by the first clustering module 430 can also depend on the features selected within each domain, how feature values are represented, how the features are combined, availability of training data, tolerance to missing values, tolerance to irrelevant features, tolerance to redundant features, tolerance to highly interdependent features, network topology, and numerous other factors. The appropriate machine learning system will be apparent to those of ordinary skill in the art when practicing the various embodiments.
In some embodiments, the clustering stage 308 can include processing for late feature fusion.
Late fusion module 422 receives outputs from the flow-based learner 452, the process-based learner 454, and the host-based learner 456, and may combine the outputs for second clustering module 432, or the second clustering module 432 can evaluate the outputs from the flow-based learner 452, the process-based learner 454, and the host-based learner 456 separately. The second clustering module 432 can implement the same or a different machine learning algorithm from the machine learning algorithm(s) of the flow-based learner 452, the process-based learner 454, and the host-based learner 456. In some embodiments, the late fusion module 422 may normalize the outputs from the flow-based learner 452, the process-based learner 454, and the host-based learner 456 and concatenate them for analysis by the cluster module 432. In other embodiments, the results of an individual domain-specific learner may be a similarity vector which can be combined with the results from other domain-specific learners to form a similarity matrix, such as by averaging, percentiling or binning, weighted averaging, etc. In averaging, each similarity (or distance) score of a first similarity (or distance) vector may be averaged with a corresponding similarity (or distance) score of a second similarity (or distance) vector to yield a single similarity (or distance) vector. Weighted averaging applies different weights to similarity scores or vectors. Weights can be user-specified or automatically obtained from automated cluster evaluations, such as via silhouette scores. Percentiling or binning maps similarity scores or vectors (or distance scores or vectors) to percentiles to account for different similarity (or distance) score distributions. In some embodiments, percentiling may be limited to nodes corresponding to the highest similarity scores (or lowest distance scores). In some embodiments, the late fusion module 422 can use various set operations to combine respective clustering results from each of the flow learner 452, the process learner 454, and the host learner 456. For example, the late fusion module 422 may define clusters as the intersections, unions, or complements of respective clusters determined by the flow learner 452, the process learner 454, and the host learner 456. That is, if a first cluster determined by a first learner includes nodes {1, 2, 3, 4} and a second cluster determined by a second learner includes nodes {3, 4, 5}, then using the intersection operation may result in clusters {1}, {2}, {3, 4}, and {5}. On the other hand, applying the union operation to the first and second clusters may yield a single cluster {1, 2, 3, 4, 5}. As will be understood by one of ordinary skill, various combinations of set operations can be utilized by the late module 422 for combining respective clusters determined by each of the flow learner 452, the process learner 454, and the host learner 456.
The late fusion module 422 can analyze the similarity matrices of two or more nodes and compare relative similarities among the nodes to arrive at the optimal number of clusters and the optimal clustering. The clustering module 432 may use any suitable machine learning algorithm depending on the various factors discussed herein (e.g., feature selection, feature representation, tolerances, etc.).
After clusters are identified, the data pipeline 300 can include a post-processing stage 310. The post-processing stage 310 can include tasks such as naming or labeling clusters, which may be automatic or user-specified; identifying cluster edges; and validating the clusters, such as by calculating silhouette scores. Silhouette scoring is a method of interpretation and validation of consistency within clusters of data. A silhouette score is a measure of how similar an object is to its own cluster compared to other clusters, which can range from −1 to 1, where a high value indicates that the node is well matched to its own cluster and badly matched to neighboring clusters. If most nodes have a high silhouette score, then the clustering maybe accurate. If many nodes have a low or negative silhouette score, then the clustering may have too many or too few clusters. The silhouette score can be calculated with any similarity or distance metric, such as the Euclidean distance or the Manhattan distance.
The end of the data pipeline 300 is a presentation stage 312 in which clustering data can be meaningfully and intuitively displayed to the user. In some embodiments, a user interface of the presentation stage 312 may allow the user to view statistics on clusters (e.g., number of nodes, edges, clusters, summaries of changes in clustering from the last ADM run, etc.) and detailed information for each cluster (e.g., nodes, server ports, and client ports, etc.). In some embodiments, the user interface may also allow the user to edit clusters (e.g., add or modify names and descriptions of clusters, move nodes from one cluster to another, approve an automatically determined cluster). In some embodiments, the user may operate the user interface to create application profiles, perform ADM re-runs, and/or export policies for cluster edges. It should be understood that the data pipeline 300 is only an example and that stages may be added, combined, removed, or modified without departing from the scope of the various embodiments.
In the embodiment of
As discussed, a sensor network can collect the captured data from multiple perspectives to provide a comprehensive view of network behavior. The sensor network may include sensors at multiple nodes of a data path (e.g., network devices, physical servers) and within multiple partitions of a node (e.g., hypervisor, shared kernel, VM, container, etc.).
After collection of the multi-domain data, the process 500 may continue on to step 504, in which the network extracts feature values from the multi-domain data for each node. For example, features from the network domain may include packet header fields such as source address, source port, destination address, destination port, protocol type, class of service, etc. and/or 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, etc.
Features from the host domain may include the host 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 the host.
Features from the process domain may include the process name, ID, parent process ID, process path, 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 process owner.
Features from the user domain can include the 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.
Features from the virtualization domain can include the name of the virtualization platform and version or build number, configuration information, host information (e.g., host operating system (OS), manufacturer, model, processor type, CPU cores, memory capacity, boot time, and other features similar to those of the host domain), a list of running VMs or containers, tenant information (e.g., tenant name, permissions, users associated with the tenant and individual user's information), and individual guest information (e.g., VM or container name, guest OS, guest memory utilization, guest CPU utilization, and other features similar to those of the host domain).
In some embodiments, out-of-band data and third party data may be collected and associated with features of one or more domains. For example, out-of-band data such as power level, temperature, physical location may be features of the host domain. As another example, third party information, such as data from a CMDB or CMS may be features of many (if not all) domains, including the network, host, virtualization, process, and user domains.
Feature values may be binary, numeric, categorical, character-based, or other primitive data types. Feature values may also be composites of primitive data types or abstract data types. Feature values may be structured or unstructured.
At step 506, the network can determine a feature vector representation for each node. Although the process 500 refers to a vector representation for features, it will be appreciated that other representations are equivalent to vectors (e.g., lists, arrays, matrices, etc.) and/or other representations may be suitable for representing features and can be utilized in various embodiments (e.g., trees, hashes, graphs, histograms, etc.).
In some embodiments, the network may utilize early fusion. For example, individual feature vectors for multiple domains can be extracted for a node, and the individual feature vectors can be combined into a single or monolithic feature vector representing the node across multiple domains. In some embodiments, the network may perform various pre-processing tasks to “stage” the individual feature vectors for fusion. These tasks may include calculating feature values (e.g., tabulating the number of flows for a node, calculating tf-idf for string-based features, etc.); encoding/decoding feature values, such as for data compression or data parallelism; and normalizing the individual feature vectors (e.g., l2 normalization).
In some embodiments, the network may implement late fusion for clustering. After individual feature vectors have been extracted during step 504, domain-specific learners may receive domain-specific feature vectors and determine similarities to other nodes at the domain level. Similarity can be a measure of how much alike two nodes are relative to other nodes, or a measure of two nodes being less distant to one another than other nodes. Similarity can be measured according to Euclidean distance, Manhattan distance, Minkowski distance, cosine similarity, or Jaccard similarity, among other similarity or distance functions. In an embodiment, the output of an individual domain-specific learner can be a similarity score or vector between a pair of nodes in one domain, and the aggregate similarity scores or vectors across all domains can represent a similarity vector or matrix for the pair of nodes. In such embodiment, the feature vector representation can correspond to the similarity vector or matrix for the pair nodes.
After the network determines the feature vector (or matrix) representation, which may correspond to a single node, a pair of nodes, or multiple nodes in various embodiments, the process 500 may proceed to step 508. At step 508, the network can analyze the feature vector (or matrix) representations determined during step 506 to assess similarity among the nodes (e.g., all nodes or selected nodes and dependency nodes). In some embodiments, the network may implement early fusion in which a feature vector corresponds to a single node. Therefore pair-wise similarity scores can be determined from a pair of feature vectors for a pair of nodes.
In other embodiments, the network may utilize late fusion. In some such embodiments, the network can optimize similarity determinations within each domain by using domain-specific similarity measures and machine learning algorithms most suitable for the features in a particular domain. For example, in an embodiment, similarity for a first domain-specific learner may be based on k-means clustering in which smaller Euclidean distances between nodes correlates to greater similarity, similarity for a second domain-specific learner may be based on hierarchical clustering in which greater cosine similarity correlates to greater similarity, and similarity for a third domain-specific learner may be based a density clustering method in which greater Euclidean distances between nodes correlates to greater similarity. In other embodiments, the network may vary averaging techniques for assessing similarity, such as by using averaging, weighted averaging, or percentiling/binning for various cluster combinations.
The process 500 may conclude at step 510, in which the network utilizes machine learning (e.g., k-means clustering, EM, DBScan, decision trees, etc.) to analyze the similarities among nodes to determine the optimal clustering.
In some embodiments, the network can present the cluster information via a user interface for displaying statistics related to clusters (e.g., number of nodes, edges, clusters, summaries of changes in clustering from the last ADM run, etc.) and other information for each cluster (e.g., nodes, server ports, and client ports, etc.). In some embodiments, the user interface may also allow the user to edit clusters (e.g., add or modify names and descriptions of clusters, move nodes from one cluster to another, approve an automatically determined cluster). In some embodiments, the user interface can be used to automatically generate application profiles or policies.
To enable user interaction with the computing system 600, an input device 645 can represent any number of input mechanisms, such as a microphone for speech, a touch-protected screen for gesture or graphical input, keyboard, mouse, motion input, speech and so forth. An output device 635 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 computing system 600. The communications interface 640 can govern and manage the user input and system output. There may be 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 630 can be 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) 625, read only memory (ROM) 620, and hybrids thereof.
The storage device 630 can include software modules 632, 634, 636 for controlling the processor 610. Other hardware or software modules are contemplated. The storage device 630 can be connected to the system bus 605. 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 610, bus 605, output device 635, and so forth, to carry out the function.
The chipset 660 can also interface with one or more communication interfaces 690 that can have different physical interfaces. The communication interfaces 690 can include interfaces for wired and wireless LANs, for broadband wireless networks, as well as personal area networks. Some applications of the methods for generating, displaying, and using the GUI disclosed herein can include receiving ordered datasets over the physical interface or be generated by the machine itself by processor 655 analyzing data stored in the storage device 670 or the RAM 675. Further, the computing system 600 can receive inputs from a user via the user interface components 685 and execute appropriate functions, such as browsing functions by interpreting these inputs using the processor 655.
It will be appreciated that computing systems 600 and 650 can have more than one processor 610 and 655, respectively, or be part of a group or cluster of computing devices networked together to provide greater processing capability.
For clarity of explanation, in some instances the various embodiments 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, mediums, 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.
This application claims priority to U.S. Provisional Application 62/171,899, titled System for Monitoring and Managing Datacenters and filed at Jun. 5, 2015, the disclosure of which is incorporated herein by reference in its entirety.
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| Number | Date | Country | |
|---|---|---|---|
| 20160359680 A1 | Dec 2016 | US |
| Number | Date | Country | |
|---|---|---|---|
| 62171899 | Jun 2015 | US |
