Auto update of sensor configuration

Abstract

Systems, methods, and computer-readable media for updating configurations in sensors deployed in multi-layer virtualized environments. In some examples, a system can track information of sensors and collectors in the network. In response to determining that a specific collector becomes unavailable (e.g., the specific collector is down, offline or becomes unsupported), the system can determine affected sensors corresponding to the specific collector, determine a new collector among active collectors of the network for each of the affected sensors, and dynamically update configuration and settings of the affected sensors to maintain proper collector-to-sensor mappings and other settings on the affected sensors.

Description

TECHNICAL FIELD

The present technology pertains to network analytics, and more specifically to updating configurations of sensors deployed in multi-layer virtualized environments.

BACKGROUND

In a network environment, sensors can be placed at various devices or elements in the network to collect flow data and network statistics from different locations. The collected data from the sensors can be analyzed to monitor and troubleshoot the network. The data collected from the sensors can provide valuable details about the status, security, or performance of the network, as well as any network elements. The sensors need to be configured to properly function and report out collected data.

However, a network environment could change dynamically or experience certain issues. As the network environment changes, sensors of the network have to be updated accordingly such that the sensors can function properly. It's a challenge to dynamically update and maintain proper configurations and settings of a large number of sensors.

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 a diagram of an example network environment, according to some examples;

FIG. 2A illustrates a schematic diagram of an example sensor deployment in a virtualized environment, according to some examples;

FIG. 2B illustrates a schematic diagram of an example sensor deployment in an example network device, according to some examples;

FIG. 2C illustrates a schematic diagram of an example reporting system in an example sensor topology, according to some examples;

FIG. 3 illustrates a schematic diagram of an example configuration for collecting sensor reports, according to some examples;

FIG. 4 illustrates an example method for updating sensors in a network, according to some examples;

FIG. 5 illustrates an example network device, according to some examples; and

FIGS. 6A and 6B illustrate example system examples.

DESCRIPTION OF EXAMPLES

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 may be used without parting from the spirit and scope of the disclosure.

Overview

Additional features and advantages of the disclosure will be set forth in the description which follows. 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.

The approaches set forth herein can be used to deploy sensors in a network environment, sense network flows, and analyze data packets reported from the sensors to monitor and troubleshoot the network. Sensors can be placed at various devices or components (e.g., virtual machines (VMs), hypervisors, and physical switches) in the network to sense network-flow information and report sensed network-flow information to corresponding collectors. A system can track information of sensors and collectors in the network. In response to determining that a specific collector becomes unavailable (e.g., the specific collector is down, offline or becomes unsupported), the system can determine affected sensors corresponding to the specific collector, determine a new collector among active collectors of the network for each of the affected sensors, and dynamically update configuration and settings of the affected sensors to maintain proper collector-to-sensor mappings and other settings on the affected sensors.

In some examples, a system can dynamically track information of collectors in a network. The information of the collectors may include, but is not limited to, status, location, and collector-to-sensor mappings. The system may further track and maintain a list of active collectors in the network. When a specific collector is down, the system can remove the specific collector from the list of active collectors. On the other hand, when a new collector is added to the network, the system can collect information of the new collector, and further determine whether the new collector is active based upon collected information of the new collector. If the new collector is active, the system can add the new collector to the list of active collectors.

In some examples, a system can determine information of a specific collector by analyzing network data pushed from the specific collector to a monitoring system in the network. The system may further determine loadings of active collectors based at least upon the network data pushed from the active collectors. In response to determining that an active collector becomes unavailable, the system may reassign affected sensors to the remaining active collectors based at least upon the loadings of the active collectors.

In some examples, a system can monitor health information of collectors in a network. The health information may include, but is not limited to, memory usage, central processing unit (CPU) utilization, bandwidth, and errors. In response to determining that health of a specific collector deteriorates beyond a threshold level, the system may remove the specific collector from a list of active collectors and reassign affected sensors to the remaining active collectors.

In some examples, a system can dynamically track information of sensors in a network. The information of the sensors may include locations, characteristics and context of the sensors. For example, the system can monitor and analyze traffic data from a specific sensor to determine whether the specific sensor resides in a virtual machine (VM), a hypervisor, or a switch, and further identify an underlying environment (i.e., operating system (OS)) of the specific sensor. In some examples, in response to determining that a specific collector becomes unavailable, the system may reassign affected sensors to remaining active collectors based at least upon the information of the affected sensors and information of the remaining active collectors.

In some examples, a system can analyze traffic data between a sensor and corresponding collector in a network. In response to determining that there is a communication issue between the sensor and the collector (e.g., the sensor has a problem contacting the collector), the system can dynamically reassign the senor to other active collectors in the network.

DETAILED DESCRIPTION

The disclosed technology addresses the need in the art for detecting compromised sensors deployed at multiple layers of a network. Disclosed are systems, methods, and computer-readable storage media for detecting and correcting sensor exploits in a network. A description of an example network environment, as illustrated in FIG. 1, is first disclosed herein. A discussion of sensors will then follow. The discussion continues with a discussion of dynamically updating configurations and settings of sensors in a network. The discussion then concludes with a description of example systems and devices. These variations shall be described herein as the various embodiments are set forth. The disclosure now turns to FIG. 1.

FIG. 1 illustrates a diagram of example network environment 100. Fabric 112 can represent the underlay (i.e., physical network) of network environment 100. Fabric 112 can include spine routers 1-N (102_A-N) (collectively “102”) and leaf routers 1-N (104_A-N) (collectively “104”). Leaf routers 104 can reside at the edge of fabric 112, and can thus represent the physical network edges. Leaf routers 104 can be, for example, top-of-rack (“ToR”) switches, aggregation switches, gateways, ingress and/or egress switches, provider edge devices, and/or any other type of routing or switching device.

Leaf routers 104 can be responsible for routing and/or bridging tenant or endpoint packets and applying network policies. Spine routers 102 can perform switching and routing within fabric 112. Thus, network connectivity in fabric 112 can flow from spine routers 102 to leaf routers 104, and vice versa.

Leaf routers 104 can provide servers 1-4 (106_A-D) (collectively “106”), hypervisors 1-4 (108_A-108_D) (collectively “108”), virtual machines (VMs) 1-4 (110_A-110_D) (collectively “110”), collectors 118, engines 120, and the Layer 2 (L2) network access to fabric 112. For example, leaf routers 104 can encapsulate and decapsulate packets to and from servers 106 in order to enable communications throughout environment 100. Leaf routers 104 can also connect other network-capable device(s) or network(s), such as a firewall, a database, a server, etc., to the fabric 112. Leaf routers 104 can also provide any other servers, resources, endpoints, external networks, VMs, services, tenants, or workloads with access to fabric 112.

VMs 110 can be virtual machines hosted by hypervisors 108 running on servers 106. VMs 110 can include workloads running on a guest operating system on a respective server. Hypervisors 108 can provide a layer of software, firmware, and/or hardware that creates and runs the VMs 110. Hypervisors 108 can allow VMs 110 to share hardware resources on servers 106, and the hardware resources on servers 106 to appear as multiple, separate hardware platforms. Moreover, hypervisors 108 and servers 106 can host one or more VMs 110. For example, server 106_A and hypervisor 108_A can host VMs 110_A-B.

In some cases, VMs 110 and/or hypervisors 108 can be migrated to other servers 106. For example, VM 110_A can be migrated to server 106_C and hypervisor 108_B. Servers 106 can similarly be migrated to other locations in network environment 100. For example, a server connected to a specific leaf router can be changed to connect to a different or additional leaf router. In some cases, some or all of servers 106, hypervisors 108, and/or VMs 110 can represent tenant space. Tenant space can include workloads, services, applications, devices, and/or resources that are associated with one or more clients or subscribers. Accordingly, traffic in network environment 100 can be routed based on specific tenant policies, spaces, agreements, configurations, etc. Moreover, addressing can vary between one or more tenants. In some configurations, tenant spaces can be divided into logical segments and/or networks and separated from logical segments and/or networks associated with other tenants.

Any of leaf routers 104, servers 106, hypervisors 108, and VMs 110 can include sensor 116 configured to capture network data, and report any portion of the captured data to collector 118. Sensors 116 can be processes, agents, modules, drivers, or components deployed on a respective system or system layer (e.g., a server, VM, virtual container, hypervisor, leaf router, etc.), configured to capture network data for the respective system (e.g., data received or transmitted by the respective system), and report some or all of the captured data and statistics to collector 118.

For example, a VM sensor can run as a process, kernel module, software element, or kernel driver on the guest operating system installed in a VM and configured to capture and report data (e.g., network and/or system data) processed (e.g., sent, received, generated, etc.) by the VM.

A hypervisor sensor can run as a process, kernel module, software element, or kernel driver on the host operating system installed at the hypervisor layer and configured to capture and report data (e.g., network and/or system data) processed (e.g., sent, received, generated, etc.) by the hypervisor.

A container sensor can run as a process, kernel module, software element, or kernel driver on the operating system of a device, such as a switch or server, which can be configured to capture and report data processed by the container.

A server sensor can run as a process, kernel module, software element, or kernel driver on the host operating system of a server and configured to capture and report data (e.g., network and/or system data) processed (e.g., sent, received, generated, etc.) by the server.

A network device sensor can run as a process, software element, or component in a network device, such as leaf routers 104, and configured to capture and report data (e.g., network and/or system data) processed (e.g., sent, received, generated, etc.) by the network device.

Sensors 116 can be configured to report observed data, statistics, and/or metadata about one or more packets, flows, communications, processes, events, and/or activities to collector 118. For example, sensors 116 can capture network data and statistics processed (e.g., sent, received, generated, dropped, forwarded, etc.) by the system or host (e.g., server, hypervisor, VM, container, switch, etc.) of the sensors 116 (e.g., where the sensors 116 are deployed). The sensors 116 can also report the network data and statistics to one or more devices, such as collectors 118 and/or engines 120. For example, the sensors 116 can report an amount of traffic processed by their host, a frequency of the traffic processed by their host, a type of traffic processed (e.g., sent, received, generated, etc.) by their host, a source or destination of the traffic processed by their host, a pattern in the traffic, an amount of traffic dropped or blocked by their host, types of requests or data in the traffic received, discrepancies in traffic (e.g., spoofed addresses, invalid addresses, hidden sender, etc.), protocols used in communications, type or characteristics of responses to traffic by the hosts of the sensors 116, what processes have triggered specific packets, etc.

Sensors 116 can also capture and report information about the system or host of the sensors 116 (e.g., type of host, type of environment, status of host, conditions of the host, etc.). Such information can include, for example, data or metadata of active or previously active processes of the system, operating system user identifiers, kernel modules loaded or used, network software characteristics (e.g., software switch, virtual network card, etc.), metadata of files on the system, system alerts, number and/or identity of applications at the host, domain information, networking information (e.g., address, topology, settings, connectivity, etc.), session information (e.g., session identifier), faults or errors, memory or CPU usage, threads, filename and/or path, services, security information or settings, and so forth.

Sensors 116 may also analyze the processes running on the respective VMs, hypervisors, servers, or network devices to determine specifically which process is responsible for a particular flow of network traffic. Similarly, sensors 116 may determine which operating system user (e.g., root, system, John Doe, Admin, etc.) is responsible for a given flow. Reported data from sensors 116 can provide details or statistics particular to one or more tenants or customers. For example, reported data from a subset of sensors 116 deployed throughout devices or elements in a tenant space can provide information about the performance, use, quality, events, processes, security status, characteristics, statistics, patterns, conditions, configurations, topology, and/or any other information for the particular tenant space.

Collectors 118 can be one or more devices, modules, workloads, VMs, containers, and/or processes capable of receiving data from sensors 116. Collectors 118 can thus collect reports and data from sensors 116. Collectors 118 can be deployed anywhere in network environment 100 and/or even on remote networks capable of communicating with network environment 100. For example, one or more collectors can be deployed within fabric 112, on the L2 network, or on one or more of the servers 106, VMs 110, hypervisors. Collectors 118 can be hosted on a server or a cluster of servers, for example. In some cases, collectors 118 can be implemented in one or more servers in a distributed fashion.

As previously noted, collectors 118 can include one or more collectors. Moreover, a collector can be configured to receive reported data from all sensors 116 or a subset of sensors 116. For example, a collector can be assigned to a subset of sensors 116 so the data received by that specific collector is limited to data from the subset of sensors 116. Collectors 118 can be configured to aggregate data from all sensors 116 and/or a subset of sensors 116. Further, collectors 118 can be configured to analyze some or all of the data reported by sensors 116.

Environment 100 can include one or more analytics engines 120 configured to analyze the data reported to collectors 118. For example, engines 120 can be configured to receive collected data from collectors 118, aggregate the data, analyze the data (individually and/or aggregated), generate reports, identify conditions, compute statistics, visualize reported data, troubleshoot conditions, visualize the network and/or portions of the network (e.g., a tenant space), generate alerts, identify patterns, calculate misconfigurations, identify errors, generate suggestions, generate testing, detect compromised elements (e.g., sensors 116, devices, servers, switches, etc.), and/or perform any other analytics functions.

Engines 120 can include one or more modules or software programs for performing such analytics. Further, engines 120 can reside on one or more servers, devices, VMs, nodes, etc. For example, engines 120 can be separate VMs or servers, an individual VM or server, or a cluster of servers or applications. Engines 120 can reside within the fabric 112, within the L2 network, outside of the environment 100 (e.g., WAN 114), in one or more segments or networks coupled with the fabric 112 (e.g., overlay network coupled with the fabric 112), etc. Engines 120 can be coupled with the fabric 112 via the leaf switches 104, for example.

While collectors 118 and engines 120 are shown as separate entities, this is simply a non-limiting example for illustration purposes, as other configurations are also contemplated herein. For example, any of collectors 118 and engines 120 can be part of a same or separate entity. Moreover, any of the collector, aggregation, and analytics functions can be implemented by one entity (e.g., a collector 118 or engine 120) or separately implemented by multiple entities (e.g., engines 120 and/or collectors 118).

Each of the sensors 116 can use a respective address (e.g., internet protocol (IP) address, port number, etc.) of their host to send information to collectors 118 and/or any other destination. Collectors 118 may also be associated with their respective addresses such as IP addresses. Moreover, sensors 116 can periodically send information about flows they observe to collectors 118. Sensors 116 can be configured to report each and every flow they observe or a subset of flows they observe. For example, sensors 116 can report every flow always, every flow within a period of time, every flow at one or more intervals, or a subset of flows during a period of time or at one or more intervals.

Sensors 116 can report a list of flows that were active during a period of time (e.g., between the current time and the time of the last report). The consecutive periods of time of observance can be represented as pre-defined or adjustable time series. The series can be adjusted to a specific level of granularity. Thus, the time periods can be adjusted to control the level of details in statistics and can be customized based on specific requirements or conditions, such as security, scalability, bandwidth, storage, etc. The time series information can also be implemented to focus on more important flows or components (e.g., VMs) by varying the time intervals. The communication channel between a sensor and collector 118 can also create a flow in every reporting interval. Thus, the information transmitted or reported by sensors 116 can also include information about the flow created by the communication channel.

When referring to a sensor's host herein, the host can refer to the physical device or component hosting the sensor (e.g., server, networking device, ASIC, etc.), the virtualized environment hosting the sensor (e.g., hypervisor, virtual machine, etc.), the operating system hosting the sensor (e.g., guest operating system, host operating system, etc.), and/or system layer hosting the sensor (e.g., hardware layer, operating system layer, hypervisor layer, virtual machine layer, etc.).

FIG. 2A illustrates a schematic diagram of an example sensor deployment 200 in a server 106_A. Server 106_A can execute and host one or more VMs 110_A-N(collectively “110”). VMs 110 can be configured to run workloads (e.g., applications, services, processes, functions, etc.) based on hardware resources 210 on server 106_A. VMs 110 can run on guest operating systems 204_A-N(collectively “204”) on a virtual operating platform provided by hypervisor 108_A. Each VM 110 can run a respective guest operating system 204 which can be the same or different as other guest operating systems 204 associated with other VMs 110 on server 106_A. Each of guest operating systems 204 can execute one or more processes, which may in turn be programs, applications, modules, drivers, services, widgets, etc. Moreover, each VM 110 can have one or more network addresses, such as an internet protocol (IP) address. VMs 110 can thus communicate with hypervisor 108_A, server 106_A, and/or any remote devices or networks using the one or more network addresses.

Hypervisor 108_A (otherwise known as a virtual machine manager or monitor) can be a layer of software, firmware, and/or hardware that creates and runs VMs 110. Guest operating systems 204 running on VMs 110 can share virtualized hardware resources created by hypervisor 108_A. The virtualized hardware resources can provide the illusion of separate hardware components. Moreover, the virtualized hardware resources can perform as physical hardware components (e.g., memory, storage, processor, network interface, peripherals, etc.), and can be driven by hardware resources 210 on server 106_A. Hypervisor 108_A can have one or more network addresses, such as an internet protocol (IP) address, to communicate with other devices, components, or networks. For example, hypervisor 108_A can have a dedicated IP address which it can use to communicate with VMs 110, server 106_A, and/or any remote devices or networks.

Hypervisor 108_A can be assigned a network address, such as an IP, with a global scope. For example, hypervisor 108_A can have an IP that can be reached or seen by VMs 110_A-N as well any other devices in the network environment 100 illustrated in FIG. 1. On the other hand, VMs 110 can have a network address, such as an IP, with a local scope. For example, VM 110_A can have an IP that is within a local network segment where VM 110_A resides and/or which may not be directly reached or seen from other network segments in the network environment 100.

Hardware resources 210 of server 106_A can provide the underlying physical hardware that drive operations and functionalities provided by server 106_A, hypervisor 108_A, and VMs 110. Hardware resources 210 can include, for example, one or more memory resources, one or more storage resources, one or more communication interfaces, one or more processors, one or more circuit boards, one or more buses, one or more extension cards, one or more power supplies, one or more antennas, one or more peripheral components, etc. Additional examples of hardware resources are described below with reference to FIGS. 5 and 6A-B.

Server 106_A can also include one or more host operating systems (not shown). The number of host operating systems can vary by configuration. For example, some configurations can include a dual boot configuration that allows server 106_A to boot into one of multiple host operating systems. In other configurations, server 106_A may run a single host operating system. Host operating systems can run on hardware resources 210. In some cases, hypervisor 108_A can run on, or utilize, a host operating system on server 106_A. Each of the host operating systems can execute one or more processes, which may be programs, applications, modules, drivers, services, widgets, etc.

Server 106_A can also have one or more network addresses, such as an IP address, to communicate with other devices, components, or networks. For example, server 106_A can have an IP address assigned to a communications interface from hardware resources 210, which it can use to communicate with VMs 110, hypervisor 108_A, leaf router 104_A in FIG. 1, collectors 118 in FIG. 1, and/or any remote devices or networks.

VM sensors 202_A-N (collectively “202”) can be deployed on one or more of VMs 110. VM sensors 202 can be data and packet inspection agents or sensors deployed on VMs 110 to capture packets, flows, processes, events, traffic, and/or any data flowing into, out of, or through VMs 110. VM sensors 202 can be configured to export or report any data collected or captured by the sensors 202 to a remote entity, such as collectors 118, for example. VM sensors 202 can communicate or report such data using a network address of the respective VMs 110 (e.g., VM IP address).

VM sensors 202 can capture and report any traffic (e.g., packets, flows, etc.) sent, received, generated, and/or processed by VMs 110. For example, sensors 202 can report every packet or flow of communication sent and received by VMs 110. Such communication channel between sensors 202 and collectors 108 creates a flow in every monitoring period or interval and the flow generated by Sensors 202 may be denoted as a control flow. Moreover, any communication sent or received by VMs 110, including data reported from sensors 202, can create a network flow. VM sensors 202 can report such flows in the form of a control flow to a remote device, such as collectors 118 illustrated in FIG. 1.

VM sensors 202 can report each flow separately or aggregated with other flows. When reporting a flow via a control flow, VM sensors 202 can include a Sensor identifier that identifies Sensors 202 as reporting the associated flow. VM sensors 202 can also include in the control flow a flow identifier, an IP address, a timestamp, metadata, a process ID, an OS username associated with the process ID, a host or environment descriptor (e.g., type of software bridge or virtual network card, type of host such as a hypervisor or VM, etc.), and any other information, as further described below. In addition, sensors 202 can append the process and user information (i.e., which process and/or user is associated with a particular flow) to the control flow. The additional information as identified above can be applied to the control flow as labels. Alternatively, the additional information can be included as part of a header, a trailer, or a payload.

VM sensors 202 can also report multiple flows as a set of flows. When reporting a set of flows, VM sensors 202 can include a flow identifier for the set of flows and/or a flow identifier for each flow in the set of flows. VM sensors 202 can also include one or more timestamps and other information as previously explained.

VM sensors 202 can run as a process, kernel module, or kernel driver on guest operating systems 204 of VMs 110. VM sensors 202 can thus monitor any traffic sent, received, or processed by VMs 110, any processes running on guest operating systems 204, any users and user activities on guest operating system 204, any workloads on VMs 110, etc.

Hypervisor sensor 206 can be deployed on hypervisor 108_A. Hypervisor sensor 206 can be a data inspection agent or sensor deployed on hypervisor 108_A to capture traffic (e.g., packets, flows, etc.) and/or data flowing through hypervisor 108_A. Hypervisor sensor 206 can be configured to export or report any data collected or captured by hypervisor sensor 206 to a remote entity, such as collectors 118, for example. Hypervisor sensor 206 can communicate or report such data using a network address of hypervisor 108_A, such as an IP address of hypervisor 108_A.

Because hypervisor 108_A can see traffic and data originating from VMs 110, hypervisor sensor 206 can also capture and report any data (e.g., traffic data) associated with VMs 110. For example, hypervisor sensor 206 can report every packet or flow of communication sent or received by VMs 110 and/or VM sensors 202. Moreover, any communication sent or received by hypervisor 108_A, including data reported from hypervisor sensor 206, can create a network flow. Hypervisor sensor 206 can report such flows in the form of a control flow to a remote device, such as collectors 118 illustrated in FIG. 1. Hypervisor sensor 206 can report each flow separately and/or in combination with other flows or data.

When reporting a flow, hypervisor sensor 206 can include a sensor identifier that identifies hypervisor sensor 206 as reporting the flow. Hypervisor sensor 206 can also include in the control flow a flow identifier, an IP address, a timestamp, metadata, a process ID, and any other information, as explained below. In addition, sensors 206 can append the process and user information (i.e., which process and/or user is associated with a particular flow) to the control flow. The additional information as identified above can be applied to the control flow as labels. Alternatively, the additional information can be included as part of a header, a trailer, or a payload.

Hypervisor sensor 206 can also report multiple flows as a set of flows. When reporting a set of flows, hypervisor sensor 206 can include a flow identifier for the set of flows and/or a flow identifier for each flow in the set of flows. Hypervisor sensor 206 can also include one or more timestamps and other information as previously explained, such as process and user information.

As previously explained, any communication captured or reported by VM sensors 202 can flow through hypervisor 108_A. Thus, hypervisor sensor 206 can observe and capture any flows or packets reported by VM sensors 202, including any control flows. Accordingly, hypervisor sensor 206 can also report any packets or flows reported by VM sensors 202 and any control flows generated by VM sensors 202. For example, VM Sensor 202_A on VM 1 (110_A) captures flow 1 (“F1”) and reports F1 to collector 118 on FIG. 1. Hypervisor sensor 206 on hypervisor 108_A can also see and capture F1, as F1 would traverse hypervisor 108_A when being sent or received by VM 1 (110_A). Accordingly, hypervisor sensor 206 on hypervisor 108_A can also report F1 to collector 118. Thus, collector 118 can receive a report of F1 from VM sensor 202_A on VM 1 (110_A) and another report of F1 from hypervisor sensor 206 on hypervisor 108_A.

When reporting F1, hypervisor sensor 206 can report F1 as a message or report that is separate from the message or report of F1 transmitted by VM sensor 202_A on VM 1 (110_A). However, hypervisor sensor 206 can also, or otherwise, report F1 as a message or report that includes or appends the message or report of F1 transmitted by VM sensor 202_A on VM 1 (110_A). In other words, hypervisor sensor 206 can report F1 as a separate message or report from VM sensor 202_A's message or report of F1, and/or a same message or report that includes both a report of F1 by hypervisor sensor 206 and the report of F1 by VM sensor 202_A at VM 1 (110_A). In this way, VM sensors 202 at VMs 110 can report packets or flows received or sent by VMs 110, and hypervisor sensor 206 at hypervisor 108_A can report packets or flows received or sent by hypervisor 108_A, including any flows or packets received or sent by VMs 110 and/or reported by VM sensors 202.

Hypervisor sensor 206 can run as a process, kernel module, or kernel driver on the host operating system associated with hypervisor 108_A. Hypervisor sensor 206 can thus monitor any traffic sent and received by hypervisor 108_A, any processes associated with hypervisor 108_A, etc.

Server 106_A can also have server sensor 208 running on it. Server sensor 208 can be a data inspection agent or sensor deployed on server 106_A to capture data (e.g., packets, flows, traffic data, etc.) on server 106_A. Server sensor 208 can be configured to export or report any data collected or captured by server sensor 206 to a remote entity, such as collector 118, for example. Server sensor 208 can communicate or report such data using a network address of server 106_A, such as an IP address of server 106_A.

Server sensor 208 can capture and report any packet or flow of communication associated with server 106_A. For example, sensor 208 can report every packet or flow of communication sent or received by one or more communication interfaces of server 106_A. Moreover, any communication sent or received by server 106_A, including data reported from sensors 202 and 206, can create a network flow associated with server 106_A. Server sensor 208 can report such flows in the form of a control flow to a remote device, such as collector 118 illustrated in FIG. 1. Server sensor 208 can report each flow separately or in combination. When reporting a flow, server sensor 208 can include a sensor identifier that identifies server Sensor 208 as reporting the associated flow. Server sensor 208 can also include in the control flow a flow identifier, an IP address, a timestamp, metadata, a process ID, and any other information. In addition, sensor 208 can append the process and user information (i.e., which process and/or user is associated with a particular flow) to the control flow. The additional information as identified above can be applied to the control flow as labels. Alternatively, the additional information can be included as part of a header, a trailer, or a payload.

Server sensor 208 can also report multiple flows as a set of flows. When reporting a set of flows, server sensor 208 can include a flow identifier for the set of flows and/or a flow identifier for each flow in the set of flows. Server sensor 208 can also include one or more timestamps and other information as previously explained.

Any communications captured or reported by sensors 202 and 206 can flow through server 106_A. Thus, server sensor 208 can observe or capture any flows or packets reported by sensors 202 and 206. In other words, network data observed by sensors 202 and 206 inside VMs 110 and hypervisor 108_A can be a subset of the data observed by server sensor 208 on server 106_A. Accordingly, server sensor 208 can report any packets or flows reported by sensors 202 and 206 and any control flows generated by sensors 202 and 206. For example, sensor 202_A on VM 1 (110_A) captures flow 1 (F1) and reports F1 to collector 118 as illustrated on FIG. 1. Sensor 206 on hypervisor 108_A can also observe and capture F1, as F1 would traverse hypervisor 108_A when being sent or received by VM 1 (110_A). In addition, sensor 206 on server 106_A can also see and capture F1, as F1 would traverse server 106_A when being sent or received by VM 1 (110_A) and hypervisor 108_A. Accordingly, sensor 208 can also report F1 to collector 118. Thus, collector 118 can receive a report (i.e., control flow) regarding F1 from sensor 202_A on VM 1 (110_A), sensor 206 on hypervisor 108_A, and sensor 208 on server 106_A.

When reporting F1, server sensor 208 can report F1 as a message or report that is separate from any messages or reports of F1 transmitted by sensor 202_A on VM 1 (110_A) or sensor 206 on hypervisor 108_A. However, server sensor 208 can also, or otherwise, report F1 as a message or report that includes or appends the messages or reports or metadata of F1 transmitted by sensor 202_A on VM 1 (110_A) and sensor 206 on hypervisor 108_A. In other words, server sensor 208 can report F1 as a separate message or report from the messages or reports of F1 from sensor 202_A and sensor 206, and/or a same message or report that includes a report of F1 by sensor 202_A, sensor 206, and sensor 208. In this way, sensors 202 at VMs 110 can report packets or flows received or sent by VMs 110, sensor 206 at hypervisor 108_A can report packets or flows received or sent by hypervisor 108_A, including any flows or packets received or sent by VMs 110 and reported by sensors 202, and sensor 208 at server 106_A can report packets or flows received or sent by server 106_A, including any flows or packets received or sent by VMs 110 and reported by sensors 202, and any flows or packets received or sent by hypervisor 108_A and reported by sensor 206.

Server sensor 208 can run as a process, kernel module, or kernel driver on the host operating system or a hardware component of server 106_A. Server sensor 208 can thus monitor any traffic sent and received by server 106_A, any processes associated with server 106_A, etc.

In addition to network data, sensors 202, 206, and 208 can capture additional information about the system or environment in which they reside. For example, sensors 202, 206, and 208 can capture data or metadata of active or previously active processes of their respective system or environment, operating system user identifiers, metadata of files on their respective system or environment, timestamps, network addressing information, flow identifiers, sensor identifiers, etc. sensors 202, 206, and 208

Moreover, sensors 202, 206, 208 are not specific to any operating system environment, hypervisor environment, network environment, or hardware environment. Thus, sensors 202, 206, and 208 can operate in any environment.

As previously explained, sensors 202, 206, and 208 can send information about the network traffic they observe. This information can be sent to one or more remote devices, such as one or more servers, collectors, engines, etc. Each sensor can be configured to send respective information using a network address, such as an IP address, and any other communication details, such as port number, to one or more destination addresses or locations. Sensors 202, 206, and 208 can send metadata about one or more flows, packets, communications, processes, events, etc.

Sensors 202, 206, and 208 can periodically report information about each flow or packet they observe. The information reported can contain a list of flows or packets that were active during a period of time (e.g., between the current time and the time at which the last information was reported). The communication channel between the sensor and the destination can create a flow in every interval. For example, the communication channel between sensor 208 and collector 118 can create a control flow. Thus, the information reported by a sensor can also contain information about this control flow. For example, the information reported by sensor 208 to collector 118 can include a list of flows or packets that were active at hypervisor 108_A during a period of time, as well as information about the communication channel between sensor 206 and collector 118 used to report the information by sensor 206.

FIG. 2B illustrates a schematic diagram of example sensor deployment 220 in an example network device. The network device is described as leaf router 104_A, as illustrated in FIG. 1. However, this is for explanation purposes. The network device can be any other network device, such as any other switch, router, etc.

In this example, leaf router 104_A can include network resources 222, such as memory, storage, communication, processing, input, output, and other types of resources. Leaf router 104_A can also include operating system environment 224. The operating system environment 224 can include any operating system, such as a network operating system, embedded operating system, etc. Operating system environment 224 can include processes, functions, and applications for performing networking, routing, switching, forwarding, policy implementation, messaging, monitoring, and other types of operations.

Leaf router 104_A can also include sensor 226. Sensor 226 can be an agent or sensor configured to capture network data, such as flows or packets, sent received, or processed by leaf router 104_A. Sensor 226 can also be configured to capture other information, such as processes, statistics, users, alerts, status information, device information, etc. Moreover, sensor 226 can be configured to report captured data to a remote device or network, such as collector 118 shown in FIG. 1, for example. Sensor 226 can report information using one or more network addresses associated with leaf router 104_A or collector 118. For example, sensor 226 can be configured to report information using an IP assigned to an active communications interface on leaf router 104_A.

Leaf router 104_A can be configured to route traffic to and from other devices or networks, such as server 106_A. Accordingly, sensor 226 can also report data reported by other sensors on other devices. For example, leaf router 104_A can be configured to route traffic sent and received by server 106_A to other devices. Thus, data reported from sensors deployed on server 106_A, such as VM and hypervisor sensors on server 106_A, would also be observed by sensor 226 and can thus be reported by sensor 226 as data observed at leaf router 104_A. Such report can be a control flow generated by sensor 226. Data reported by the VM and hypervisor sensors on server 106_A can therefore be a subset of the data reported by sensor 226.

Sensor 226 can run as a process or component (e.g., firmware, module, hardware device, etc.) in leaf router 104_A. Moreover, sensor 226 can be installed on leaf router 104_A as a software or firmware agent. In some configurations, leaf router 104_A itself can act as sensor 226. Moreover, sensor 226 can run within operating system 224 and/or separate from operating system 224.

FIG. 2C illustrates a schematic diagram of example reporting system 240 in an example sensor topology. The sensor topology includes sensors along a path from a virtualized environment (e.g., VM and hypervisor) to the fabric 112.

Leaf router 104_A can route packets or traffic 242 between fabric 112 and server 106_A, hypervisor 108_A, and VM 110_A. Packets or traffic 242 between VM 110_A and leaf router 104_A can flow through hypervisor 108_A and server 106_A. Packets or traffic 242 between hypervisor 108_A and leaf router 104_A can flow through server 106_A. Finally, packets or traffic 242 between server 106_A and leaf router 104_A can flow directly to leaf router 104_A. However, in some cases, packets or traffic 242 between server 106_A and leaf router 104_A can flow through one or more intervening devices or networks, such as a switch or a firewall.

Moreover, VM sensor 202_A at VM 110_A, hypervisor sensor 206_A at hypervisor 108_A, network device sensor 226 at leaf router 104_A, and any server sensor at server 106_A (e.g., sensor running on host environment of server 106_A) can send reports 244 (also referred to as control flows) to collector 118 based on the packets or traffic 242 captured at each respective sensor. Reports 244 from VM sensor 202_A to collector 118 can flow through VM 110_A, hypervisor 108_A, server 106_A, and leaf router 104_A. Reports 244 from hypervisor sensor 206_A to collector 118 can flow through hypervisor 108_A, server 106_A, and leaf router 104_A. Reports 244 from any other server sensor at server 106_A to collector 118 can flow through server 106_A and leaf router 104_A. Finally, reports 244 from network device sensor 226 to collector 118 can flow through leaf router 104_A. Although reports 244 are depicted as being routed separately from traffic 242 in FIG. 2C, one of ordinary skill in the art will understand that reports 244 and traffic 242 can be transmitted through the same communication channel(s).

Reports 244 can include any portion of packets or traffic 242 captured at the respective sensors. Reports 244 can also include other information, such as timestamps, process information, sensor identifiers, flow identifiers, flow statistics, notifications, logs, user information, system information, etc. Some or all of this information can be appended to reports 244 as one or more labels, metadata, or as part of the packet(s)′ header, trailer, or payload. For example, if a user opens a browser on VM 110_A and navigates to examplewebsite.com, VM sensor 202_A of VM 110_A can determine which user (i.e., operating system user) of VM 110_A (e.g., username “johndoe85”) and which process being executed on the operating system of VM 110_A (e.g., “chrome.exe”) were responsible for the particular network flow to and from examplewebsite.com. Once such information is determined, the information can be included in report 244 as labels for example, and report 244 can be transmitted from VM sensor 202_A to collector 118. Such additional information can help system 240 to gain insight into flow information at the process and user level, for instance. This information can be used for security, optimization, and determining structures and dependencies within system 240.

In some examples, the reports 244 can include various statistics and/or usage information reported by the respective sensors. For example, the reports 244 can indicate an amount of traffic captured by the respective sensor, which can include the amount of traffic sent, received, and generated by the sensor's host; a type of traffic captured, such as video, audio, Web (e.g., HTTP or HTTPS), database queries, application traffic, etc.; a source and/or destination of the traffic, such as a destination server or application, a source network or device, a source or destination address or name (e.g., IP address, DNS name, FQDN, packet label, MAC address, VLAN, VNID, VxLAN, source or destination domain, etc.); a source and/or destination port (e.g., port 25, port 80, port 443, port 8080, port 22); a traffic protocol; traffic metadata; etc. The reports 244 can also include indications of traffic or usage patterns and information, such as frequency of communications, intervals, type of requests, type of responses, triggering processes or events (e.g., causality), resource usage, etc.

Each of the sensors 202_A, 206_A, 226 can include a respective unique sensor identifier on each of reports 244 it sends to collector 118, to allow collector 118 to determine which sensor sent the report. Sensor identifiers in reports 244 can also be used to determine which sensors reported what flows. This information can then be used to determine sensor placement and topology, as further described below, as well as mapping individual flows to processes and users. Such additional insights gained can be useful for analyzing the data in reports 244, as well as troubleshooting, security, visualization, configuration, planning, and management, and so forth.

As previously noted, the topology of the sensors can be ascertained from the reports 244. To illustrate, a packet received by VM 110_A from fabric 112 can be captured and reported by VM sensor 202_A. Since the packet received by VM 110_A will also flow through leaf router 104_A and hypervisor 108_A, it can also be captured and reported by hypervisor sensor 206_A and network device sensor 226. Thus, for a packet received by VM 110_A from fabric 112, collector 118 can receive a report of the packet from VM sensor 202_A, hypervisor sensor 206_A, and network device sensor 226.

Similarly, a packet sent by VM 110_A to fabric 112 can be captured and reported by VM sensor 202_A. Since the packet sent by VM 110_A will also flow through leaf router 104_A and hypervisor 108_A, it can also be captured and reported by hypervisor sensor 206_A and network device sensor 226. Thus, for a packet sent by VM 110_A to fabric 112, collector 118 can receive a report of the packet from VM sensor 202_A, hypervisor sensor 206_A, and network device sensor 226.

On the other hand, a packet originating at, or destined to, hypervisor 108_A, can be captured and reported by hypervisor sensor 206_A and network device sensor 226, but not VM sensor 202_A, as such packet may not flow through VM 110_A. Moreover, a packet originating at, or destined to, leaf router 104_A, will be captured and reported by network device sensor 226, but not VM sensor 202_A, hypervisor sensor 206_A, or any other sensor on server 106_A, as such packet may not flow through VM 110_A, hypervisor 108_A, or server 106_A.

Information ascertained or inferred about the topology of the sensors can also be used with the reports 244 to detect problems. For example, the inferred topology of the sensors can be used with the current and/or historical statistics included in the reports 244 to infer or detect various conditions. To illustrate, traffic to and from fabric 112 captured by VM sensor 202 should also be captured by hypervisor sensor 206 and network device sensor 226. Thus, if VM sensor 202 reports 200 packets to or from fabric 112 during a period of time and network device sensor 226 only reports 20 packets to or from fabric 112 during that same period of time, then one can infer from this discrepancy that VM sensor 202 has reported and/or captured an abnormal or unexpected number of packets during that period of time. This abnormal activity can be determined to indicate a faulty state of the VM sensor 202, such as an error, a bug, malware, a virus, or a compromised condition.

Other statistics and usage details determined from reports 244 can also be considered for determining problems or faults with sensors and/or hosts. For example, if hypervisor sensor 206 has typically reported in the past an average of 10K server hits (e.g., Web, email, database, etc.) every 7 days, and reports 244 indicate a spike of 50K server hits over the last 2 days, then one can infer that this abnormal levels of activity indicate a problem with the hypervisor sensor 206 and/or its host (i.e., hypervisor 108 or server 106). The abnormal levels of activity can be a result of malware or a virus affecting the hypervisor sensor 206.

In another example, if the reports 244 indicate that the VM sensor 202 has been generating unexpected, improper, or excessive traffic, such as sending packets or commands to a new or different device other than collector 118—or other than any other system with which VM sensor 202 is expected or configured to communicate with—or sending the wrong types of packets (e.g., other than reports 244) or sending traffic at unexpected times or events (e.g., without being triggered by a predefined setting or event such as the capturing of a packet processed by the host), then one can assume that VM sensor 202 has been compromised or is being manipulated by an unauthorized user or device.

Reports 244 can be transmitted to collector 118 periodically as new packets or traffic 242 are captured by a sensor, or otherwise based on a schedule, interval, or event, for example. Further, each sensor can send a single report or multiple reports to collector 118. For example, each of the sensors can be configured to send a report to collector 118 for every flow, packet, message, communication, or network data received, transmitted, and/or generated by its respective host (e.g., VM 110_A, hypervisor 108_A, server 106_A, and leaf router 104_A). As such, collector 118 can receive a report of a same packet from multiple sensors. In other examples, one or more sensors can be configured to send a report to collector 118 for one or more flows, packets, messages, communications, network data, or subset(s) thereof, received, transmitted, and/or generated by the respective host during a period of time or interval.

FIG. 3 illustrates a schematic diagram of an example configuration 300 for collecting sensor reports (i.e., control flows). In configuration 300, traffic between fabric 112 and VM 110_A is configured to flow through hypervisor 108_A. Moreover, traffic between fabric 112 and hypervisor 108_A is configured to flow through leaf router 104_A.

VM sensor 202_A can be configured to report to collector 118 traffic sent, received, or processed by VM 110_A. Hypervisor sensor 210 can be configured to report to collector 118 traffic sent, received, or processed by hypervisor 108_A. Finally, network device sensor 226 can be configured to report to collector 118 traffic sent, received, or processed by leaf router 104_A.

Collector 118 can thus receive flows 302 from VM sensor 202_A, flows 304 from hypervisor sensor 206_A, and flows 406 from network device sensor 226. Flows 302, 304, and 306 can include control flows. Flows 302 can include flows captured by VM sensor 202_A at VM 110_A.

Flows 304 can include flows captured by hypervisor sensor 206_A at hypervisor 108_A. Flows captured by hypervisor sensor 206_A can also include flows 302 captured by VM sensor 202_A, as traffic sent and received by VM 110_A will be received and observed by hypervisor 108_A and captured by hypervisor sensor 206_A.

Flows 306 can include flows captured by network device sensor 226 at leaf router 104_A. Flows captured by network device sensor 226 can also include flows 302 captured by VM sensor 202_A and flows 304 captured by hypervisor sensor 206_A, as traffic sent and received by VM 110_A and hypervisor 108_A is routed through leaf router 104_A and can thus be captured by network device sensor 226.

Collector 118 can collect flows 302, 304, and 306, and store the reported data. Collector 118 can also forward some or all of flows 302, 304, and 306, and/or any respective portion thereof, to engine 120. Engine 120 can process the information, including any information about the sensors (e.g., agent placement, agent environment, etc.) and/or the captured traffic (e.g., statistics), received from collector 118 to identify patterns, conditions, network or device characteristics; log statistics or history details; aggregate and/or process the data; generate reports, timelines, alerts, graphical user interfaces; detect errors, events, inconsistencies; troubleshoot networks or devices; configure networks or devices; deploy services or devices; reconfigure services, applications, devices, or networks; etc.

Collector 118 and/or engine 120 can map individual flows that traverse VM 110_A, hypervisor 108_A, and/or leaf router 104_A to the specific sensors at VM 110_A, hypervisor 108_A, and/or leaf router 104_A. For example, collector 118 or engine 120 can determine that a particular flow that originated from VM 110_A and destined for fabric 112 was sent by VM 110_A and such flow was reported by VM sensor 202. It may be determined that the same flow was received by a process named X on hypervisor 108_A and forwarded to a process named Yon leaf router 104_A and also reported by hypervisor sensor 206.

While engine 120 is illustrated as a separate entity, other configurations are also contemplated herein. For example, engine 120 can be part of collector 118 and/or a separate entity. Indeed, engine 120 can include one or more devices, applications, modules, databases, processing components, elements, etc. Moreover, collector 118 can represent one or more collectors. For example, in some configurations, collector 118 can include multiple collection systems or entities, which can reside in one or more networks.

Having disclosed some basic system components and concepts, the disclosure now turns to the example method 400 shown in FIG. 4. For the sake of clarity, the method is described in terms of collector 118 and sensors 116, as shown in FIG. 1, configured to practice the various steps in the method. However, the example methods can be practiced by any software or hardware components, devices, etc. heretofore disclosed. The steps outlined herein are exemplary and can be implemented in any combination thereof in any order, including additional, fewer, or alternative steps performed in similar or alternative orders, or in parallel. The system 100 can determine collector information of a plurality of collectors 118, at step 402. The collector information may include, but is not limited to, status, location, and collector-to-sensor mappings.

In some examples, the system 100 can extract collector information of the plurality of collectors by analyzing network data pushed from the collectors to a monitoring system. The system 100 may further determine loading information of the plurality of collectors 118 based upon the data pushed from the collectors.

The system 100 can analyze the collector information to determine active collectors among the plurality of collectors 118, at step 404. The system 100 can further determine whether any specific collector among the active collectors 118 is down, at step 406. If no collector is down, the example method 400 goes back to step 402.

In response to determining that a specific collector is down, the system 100 can remove the specific collector from the active collectors, at step 408. In some example, the system 100 can determine whether a new collector is added to the network 100 and further determine collector information of the new collector. In response to determining that a new collector is active based upon the collector information of the new collector, the system 100 can add the new collector to the active collectors.

In some examples, the system 100 may determine health information of the plurality of collectors 118 based upon the network data pushed from the collectors. In response to determining that health of a specific collector deteriorates beyond a threshold level, the system 100 can remove the specific collector from active collectors.

In some example, the system 100 can analyze traffic data between a specific collector 118 and corresponding sensors 116. In response to determining that there is a communication issue between the specific collector 118 and corresponding sensors 116 (e.g., the corresponding sensor(s) has a problem contacting the specific collector), the system can determine that the specific collector becomes unavailable to the corresponding sensor(s).

The system 100 can receive sensor information from a plurality of sensors 116, at step 410. The sensor information may include, but is not limited to, locations, characteristics and context of the plurality of sensors. Based upon the sensor information, the system 100 can determine at least one sensor 116 that corresponds to the specific collector 118, at step 412.

The system 100 can reassign the at least one sensor 116 to a new collector among the active collectors 118, at step 414. The system 100 may further cause configuration and settings of the at least one sensor 116 to be updated based at least upon information of the new collector, at step 416.

In some examples, information from the plurality of sensors or the plurality of collectors may include one or more fields, such as: flow identifier (e.g., unique identifier associated with the flow), sensor identifier (e.g., data uniquely identifying reporting sensor), timestamp (e.g., time of event, report, etc.), interval (e.g., time between current report and previous report, interval between flows or packets, interval between events, etc.), duration (e.g., duration of event, duration of communication, duration of flow, duration of report, etc.), flow direction (e.g., egress flow, ingress flow, etc.), application identifier (e.g., identifier of application associated with flow, process, event, or data), port (e.g., source port, destination port, layer 4 port, etc.), destination address (e.g., interface address associated with destination, IP address, domain name, network address, hardware address, virtual address, physical address, etc.), source address (e.g., interface address associated with source, IP address, domain name, network address, hardware address, virtual address, physical address, etc.), interface (e.g., interface address, interface information, etc.), protocol (e.g., layer 4 protocol, layer 3 protocol, etc.), event (e.g., description of event, event identifier, etc.), flag (e.g., layer 3 flag, flag options, etc.), tag (e.g., virtual local area network tag, etc.), process (e.g., process identifier, etc.), user (e.g., OS username, etc.), bytes (e.g., flow size, packet size, transmission size, etc.), and sensor Type (e.g., the type of virtualized environment hosting the Sensor, such as hypervisor or VM; the type of virtual network device, such as VNIC, LINUX bridge, OVS, software switch, etc.).

In some examples, other fields and data items are also contemplated herein, such as handshake information, system information, network address associated with sensor or host, operating system environment information, network data or statistics, process statistics, system statistics, etc. The order in which these fields are illustrated is also exemplary and can be rearranged in any other way. One or more of these fields can be part of a header, a trailer, or a payload of in one or more packets. Moreover, one or more of these fields can be applied to the one or more packets as labels. Each of the fields can include data, metadata, and/or any other information relevant to the fields.

Example Devices

FIG. 5 illustrates an example network device 500 according to some examples. Network device 500 includes a master central processing unit (CPU) 502, interfaces 504, and a bus 506 (e.g., a PCI bus). When acting under the control of appropriate software or firmware, the CPU 502 is responsible for executing packet management, error detection, and/or routing functions. The CPU 502 preferably accomplishes all these functions under the control of software including an operating system and any appropriate applications software. CPU 502 may include one or more processors 510 such as a processor from the Motorola family of microprocessors or the MIPS family of microprocessors. In an alternative example, processor 510 is specially designed hardware for controlling the operations of router. In a specific example, a memory 508 (such as non-volatile RAM and/or ROM) also forms part of CPU 502. However, there are many different ways in which memory could be coupled to the system.

The interfaces 504 are typically provided as 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 router. 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 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 and management. By providing separate processors for the communications intensive tasks, these interfaces allow the master microprocessor 502 to efficiently perform routing computations, network diagnostics, security functions, etc.

Although the system shown in FIG. 5 is one specific network device of the present invention, it is by no means the only network device architecture on which the present invention 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 router.

Regardless of the network device's configuration, it may employ one or more memories or memory modules (including memory 508) 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.

FIG. 6A and FIG. 6B illustrate example system examples. The more appropriate example will be apparent to those of ordinary skill in the art when practicing the present technology. Persons of ordinary skill in the art will also readily appreciate that other system examples are possible.

FIG. 6A illustrates a conventional system bus computing system architecture 600 wherein the components of the system are in electrical communication with each other using a bus 612. Exemplary system 600 includes a processing unit (CPU or processor) 602 and a system bus 612 that couples various system components including the system memory 606, such as read only memory (ROM) 608 and random access memory (RAM) 610, to the processor 602. The system 600 can include a cache of high-speed memory connected directly with, in close proximity to, or integrated as part of the processor 602. The system 600 can copy data from the memory 606 and/or the storage device 620 to the cache 604 for quick access by the processor 602. In this way, the cache can provide a performance boost that avoids processor 602 delays while waiting for data. These and other modules can control or be configured to control the processor 602 to perform various actions. Other system memory 606 may be available for use as well. The memory 606 can include multiple different types of memory with different performance characteristics. The processor 602 can include any general purpose processor and a hardware module or software module, such as module 1 (622), module 2 (624), and module 3 (626) stored in storage device 620, configured to control the processor 602 as well as a special-purpose processor where software instructions are incorporated into the actual processor design. The processor 602 may essentially 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 600, an input device 614 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 616 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 600. The communications interface 618 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 620 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) 610, read only memory (ROM) 608, and hybrids thereof.

The storage device 620 can include software modules 622, 624, 626 for controlling the processor 602. Other hardware or software modules are contemplated. The storage device 620 can be connected to the system bus 612. 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 602, bus 612, display 616, and so forth, to carry out the function.

FIG. 6B illustrates an example computer system 650 having a chipset architecture that can be used in executing the described method and generating and displaying a graphical user interface (GUI). Computer system 650 is an example of computer hardware, software, and firmware that can be used to implement the disclosed technology. System 650 can include a processor 652, representative of any number of physically and/or logically distinct resources capable of executing software, firmware, and hardware configured to perform identified computations. Processor 652 can communicate with a chipset 654 that can control input to and output from processor 652. In this example, chipset 654 outputs information to output device 656, such as a display, and can read and write information to storage device 658, which can include magnetic media, and solid state media, for example. Chipset 654 can also read data from and write data to RAM 660. A bridge 662 for interfacing with a variety of user interface components 664 can be provided for interfacing with chipset 654. Such user interface components 664 can include a keyboard, a microphone, touch detection and processing circuitry, a pointing device, such as a mouse, and so on. In general, inputs to system 650 can come from any of a variety of sources, machine generated and/or human generated.

Chipset 654 can also interface with one or more communication interfaces 666 that can have different physical interfaces. Such communication interfaces can include interfaces for wired and wireless local area networks, 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 652 analyzing data stored in storage 658 or 660. Further, the machine can receive inputs from a user via user interface components 664 and execute appropriate functions, such as browsing functions by interpreting these inputs using processor 652.

It can be appreciated that example systems 600 and 650 can have more than one processor 602 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 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 examples 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. Moreover, claim language reciting “at least one of” a set indicates that one member of the set or multiple members of the set satisfy the claim.

It should be understood that features or configurations herein with reference to one embodiment or example can be implemented in, or combined with, other examples or examples herein. That is, terms such as “embodiment”, “variation”, “aspect”, “example”, “configuration”, “implementation”, “case”, and any other terms which may connote an embodiment, as used herein to describe specific features or configurations, are not intended to limit any of the associated features or configurations to a specific or separate embodiment or examples, and should not be interpreted to suggest that such features or configurations cannot be combined with features or configurations described with reference to other examples, variations, aspects, examples, configurations, implementations, cases, and so forth. In other words, features described herein with reference to a specific example (e.g., embodiment, variation, aspect, configuration, implementation, case, etc.) can be combined with features described with reference to another example. Precisely, one of ordinary skill in the art will readily recognize that the various examples or examples described herein, and their associated features, can be combined with each other.

A phrase such as an “aspect” does not imply that such aspect is essential to the subject technology or that such aspect applies to all configurations of the subject technology. A disclosure relating to an aspect may apply to all configurations, or one or more configurations. A phrase such as an aspect may refer to one or more aspects and vice versa. A phrase such as a “configuration” does not imply that such configuration is essential to the subject technology or that such configuration applies to all configurations of the subject technology. A disclosure relating to a configuration may apply to all configurations, or one or more configurations. A phrase such as a configuration may refer to one or more configurations and vice versa. The word “exemplary” is used herein to mean “serving as an example or illustration.” Any aspect or design described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other aspects or designs. Moreover, claim language reciting “at least one of” a set indicates that one member of the set or multiple members of the set satisfy the claim.

Claims

1. A method comprising: first receiving sensor information from a plurality of sensors of a network at a collector of a plurality of collectors, the sensors including a virtual machine (VM) sensor for a VM, a hypervisor sensor for a hypervisor, at least one server sensor in a server different from a virtual machine sensor or a hypervisor sensor, the first receiving comprising: information from the VM sensor to the collector flows through the VM, the hypervisor, the server, and a leaf router;information from hypervisor sensor to the collector flows through the hypervisor, the server, and the leaf router;information from the at least one server sensor to the collector flows through the server and the leaf router;identifying, in response to the collector becoming unavailable, the sensors of the plurality of sensors that corresponds to an the unavailable collector of a the plurality of collectors from the sensor information;reassigning, in response to the identifying, the sensors to another collector from the plurality of collectors; andcausing the sensors to update a configuration of the sensors based on collector information associated with the another collector{circumflex over ( )}second receiving, in response to the causing, sensor information from the plurality of sensors at the another collector, the second receiving comprising: information from the VM sensor to the another collector flows through the VM, the hypervisor, the server, and a leaf router;information from hypervisor sensor to the another collector flows through the hypervisor, the server, and the leaf router; andinformation from the at least one server sensor to the another collector flows through the server and the leaf router.

2. The method of claim 1, further comprising: determining loads on the plurality of collectors operating in the network based on network data for the plurality of collectors; andthe reassigning the sensors to the another collector of the plurality of collectors is based on the loads on the plurality of collectors.

3. The method of claim 1, further comprising: identifying a health of the collector with respect to operation of the collector in the network; anddetermining that the collector is unavailable based on a comparison of the health of the collector to one or more threshold levels of collector health.

4. The method of claim 1, further comprising: identifying that a communication error exists between the collector and one or more sensors associated with the collector; anddetermining that the collector is unavailable based on existence of the communication error.

5. The method of claim 1, wherein the sensor information is received as part of sensor reports generated by the plurality of sensors.

6. The method of claim 1, wherein the sensor information of the plurality of sensors comprises location information, characteristic information, and context information of the plurality of sensors.

7. The method of claim 1, further comprising: determining that a new collector is added to the network; accessing collector information of the new collector; determining that the new collector is active based on the collector information; and

8. A system comprising: one or more processors; anda non-transitory computer-readable medium comprising instructions stored therein, which when executed by the one or more processors, cause the system to perform operations comprising: first receive sensor information from a plurality of sensors of a network at a collector of a plurality of collectors, the sensors including a virtual machine (VM) sensor for a VM, a hypervisor sensor for a hypervisor, at least one server sensor in a server different from a virtual machine sensor or a hypervisor sensor, the first receiving comprising:information from the VM sensor to the collector flows through the VM, the hypervisor, the server, and a leaf router;information from hypervisor sensor to the collector flows through the hypervisor, the server, and the leaf router;information from the at least one server sensor to the collector flows through the server and the leaf router;identify, in response to the collector becoming unavailable, the sensors of the plurality of sensors that corresponds to an the unavailable collector of a the plurality of collectors from the sensor information;reassign, in response to the identify, the sensor to another collector from the plurality of collectors; andcause the sensors to update a configuration of the sensor based on collector information associated with the another collector{circumflex over ( )}second receive, in response to the causing, sensor information from the plurality of sensors at the another collector, the second receiving comprising:information from the VM sensor to the another collector flows through the VM, the hypervisor, the server, and a leaf router;information from hypervisor sensor to the another collector flows through the hypervisor, the server, and the leaf router; andinformation from the at least one server sensor to the another collector flows through the server and the leaf router.

9. The system of claim 8, the operations further comprising: determine loads on the plurality of collectors operating in the network based on network data for the plurality of collectors; and the reassign the sensors to the another collector of the plurality of collectors is based on the loads on the plurality of collectors.

10. The system of claim 8, the operations further comprising: identify a health of the collector with respect to operation of the collector in the network; anddetermine that the collector is unavailable based on a comparison of the health of the collector to one or more threshold levels of collector health.

11. The system of claim 8, the operations further comprising: identify that a communication error exists between the collector and one or more sensors associated with the collector; anddetermine that the collector is unavailable based on existence of the communication error.

12. The system of claim 8, wherein the sensor information is received as part of sensor reports generated by the plurality of sensors.

13. The system of claim 8, wherein collectors of the plurality of collectors are configured to collect flows travelling through corresponding sensors of the plurality of sensors.

14. The system of claim 8, the operations further comprising: determine that a new collector is added to the network;access collector information of the new collector;determine that the new collector is active based on the collector information; and reassign the sensors to the new collector based on the new collector being active.

15. A non-transitory computer-readable storage medium comprising instructions stored therein, which when executed by one or more processors, cause the one or more processors to perform operations comprising: first receive sensor information from a plurality of sensors of a network at a collector of a plurality of collectors, the sensors including a virtual machine (VM) sensor for a VM, a hypervisor sensor for a hypervisor, at least one server sensor in a server different from a virtual machine sensor or a hypervisor sensor, the first receiving comprising: information from the VM sensor to the collector flows through the VM, the hypervisor, the server, and a leaf router;information from hypervisor sensor to the collector flows through the hypervisor, the server, and the leaf router;information from the at least one server sensor to the collector flows through the server and the leaf router;identify, in response to the collector becoming unavailable, the sensors of the plurality of sensors that corresponds to an the unavailable collector of a the plurality of collectors from the sensor information;reassign, in response to the identify, the sensor to another collector from the plurality of collectors; andcause the sensors to update a configuration of the sensor based on collector information associated with the another collector{circumflex over ( )}second receive, in response to the causing, sensor information from the plurality of sensors at the another collector, the second receiving comprising: information from the VM sensor to the another collector flows through the VM, the hypervisor, the server, and a leaf router;information from hypervisor sensor to the another collector flows through the hypervisor, the server, and the leaf router; andinformation from the at least one server sensor to the another collector flows through the server and the leaf router.

16. The non-transitory computer-readable storage medium of claim 15, the operations further comprising: determine loads on the plurality of collectors operating in the network based on network data for the plurality of collectors; andthe reassign the sensors to the another collector of the plurality of collectors is based on the loads on the plurality of collectors.

17. The non-transitory computer-readable storage medium of claim 16, the operations further comprising: identify a health of the collector with respect to operation of the collector in the network;anddetermine that the collector is unavailable based on a comparison of the health of the collector to one or more threshold levels of collector health.

18. The non-transitory computer-readable storage medium of claim 16, the operations further comprising: identify that a communication error exists between the collector and one or more sensors associated with the collector; anddetermine that the collector is unavailable based on existence of the communication error.

19. The non-transitory computer-readable storage medium of claim 15, wherein the sensor information is received as part of sensor reports generated by the plurality of sensors.

20. The non-transitory computer-readable storage medium of claim 15, wherein the instructions, which when executed by the one or more processors, further cause the one or more processors to: determine that a new collector is added to the network;access collector information of the new collector;determine that the new collector is active based on the collector information; and reassign the sensors to the new collector based on the new collector being active.