Abstract: This disclosure describes techniques that include collecting underlay flow data within a network and associating underlay flow data with a source and a destination virtual network to enable insights into network operation and performance. In one example, this disclosure describes a method that includes identifying, for each underlay data flow, a source overlay network and a destination overlay network associated with the underlay data flow, wherein identifying includes retrieving, from one or more Ethernet Virtual Private Network (EVPN) databases, information identifying the source and destination overlay networks.

Abstract: This disclosure describes techniques for monitoring, scheduling, and performance management for virtualization infrastructures within networks. In one example, a computing system includes a plurality of different cloud-based compute clusters (e.g., different cloud projects), each comprising a set of compute nodes. Policy agents execute on the compute nodes to monitor performance and usage metrics relating to resources of the compute nodes. Policy controllers within each cluster deploy policies to the policy agents and evaluate performance and usage metrics from the policy agents by application of one or more rulesets for infrastructure elements of the compute cluster. Each of the policy controllers outputs data to a multi-cluster dashboard software system indicative of a current health status for the infrastructure elements based on the evaluation of the performance and usage metrics for the cluster.

Abstract: A variety of different graphical user interfaces are generated that when displayed provide a visual and interactive representation of one or more performance metrics associated with the operation of a computer network. The graphical user interfaces may be used to monitor the underlay computer network for a virtualization infrastructure, as one example. Aspects include grouping the servers of a computer network into a plurality of aggregates, each aggregate comprising one or more servers. A set of probes are configured that are issued by an agent of a server in one aggregate and sent through the computer network to one or more agents in the server(s) of a different aggregate. Responses and other measurements taken based on the issuance of the probes is gathered and analyzed to generate metrics that are then used to generate, at least in part, the information provided in the graphical user interfaces.

Abstract: Techniques for resource monitoring and managed message reordering in a data center are described. In one example, a computing system comprises an ingress engine to receive a message from a network device in a data center comprising a plurality of network devices and the computing system; and in response to receiving the message from a network device in the data center, communicate the message to an appropriate collector application corresponding to the message's protocol type in compliance with at least one requirement for data stored in a message flow communicated from one or more network devices to the computing system.

Abstract: This disclosure describes techniques for monitoring, scheduling, and performance management for virtualization infrastructures within networks. In one example, a computing system includes a plurality of different cloud-based compute clusters (e.g., different cloud projects), each comprising a set of compute nodes. Policy agents execute on the compute nodes to monitor performance and usage metrics relating to resources of the compute nodes. Policy controllers within each cluster deploy policies to the policy agents and evaluate performance and usage metrics from the policy agents by application of one or more rulesets for infrastructure elements of the compute cluster. Each of the policy controllers outputs data to a multi-cluster dashboard software system indicative of a current health status for the infrastructure elements based on the evaluation of the performance and usage metrics for the cluster.

Abstract: This disclosure describes techniques that include collecting underlay flow data within a network and associating underlay flow data with a source and a destination virtual network to enable insights into network operation and performance. In one example, this disclosure describes a method that includes identifying, for each underlay data flow, a source overlay network and a destination overlay network associated with the underlay data flow, wherein identifying includes retrieving, from one or more Ethernet Virtual Private Network (EVPN) databases, information identifying the source and destination overlay networks.

Abstract: This disclosure describes techniques that include using an automatically trained machine learning system to generate a prediction. In one example, this disclosure describes a method comprising: based on a request for the prediction: training each respective machine learning (ML) model in a plurality of ML models to generate a respective training-phase prediction in a plurality of training-phase predictions; automatically determining a selected ML model in the plurality of ML models based on evaluation metrics for the plurality of ML; and applying the selected ML model to generate the prediction based on data collected from a network that includes a plurality of network devices.

Abstract: A computing system includes a computing device configured to execute a plurality of virtual machines, each virtual machine of the plurality of virtual machines configured to provide control plane functionality for at least a different respective subset of forwarding units of a network device, the computing device distinct from the network devices. The computing system also includes a policy agent configured to execute on the computing device. The agent is configured to determine that a particular virtual machine of the plurality of virtual machines provides control plane functionality for one or more forwarding units of the network device; determine control plane usage metrics for resources of the particular virtual machine; and output, to a policy controller, data associated with the control plane usage metrics and data associating the particular virtual machine with the one or more forwarding units for which the particular virtual machine provides control plane functionality.

Abstract: An alarm service can receive an alarm rule as an “intent” that defines a rule in a high level “natural language.” An alarm rule compiler can receive the intent and translate the high level intent into one or more lower level rules that can be programmatically processed by multiple alarm rule execution engines. Devices in a network system can be associated with alarm rule execution engines in a distributed manner. For example, devices in a network can be associated with different instances of an alarm rule execution engine, thus distributing the resource usage for obtaining telemetry data and processing alarms with respect to the devices in a network across multiple alarm rule execution engines.

Abstract: A computing system includes a computing device configured to execute a plurality of virtual machines, each virtual machine of the plurality of virtual machines configured to provide control plane functionality for at least a different respective subset of forwarding units of a network device, the computing device distinct from the network devices. The computing system also includes a policy agent configured to execute on the computing device. The agent is configured to determine that a particular virtual machine of the plurality of virtual machines provides control plane functionality for one or more forwarding units of the network device; determine control plane usage metrics for resources of the particular virtual machine; and output, to a policy controller, data associated with the control plane usage metrics and data associating the particular virtual machine with the one or more forwarding units for which the particular virtual machine provides control plane functionality.

Abstract: A system that permits or otherwise facilitates assessment of operational state of a computing component in a computing environment. In one example, this disclosure describes a method that includes collecting, by a server device that is executing within a server device cluster, metric information indicative of an operational state of the server device, wherein the metric information is associated with a plurality of virtual computing instances executing on the server device; analyzing, by the server device and based on the metric information, whether a first condition associated with a first virtual computing instance is satisfied; analyzing, by the server device and based on the metric information, whether a second condition associated with a second virtual computing instance is satisfied; and updating control information characterizing the operational state of the server device executing within the server device cluster.

Abstract: In one aspect, a system that provides a context-aware code security solution within a continuous integration and continuous deployment (CI/CD) pipeline is disclosed. During operation, the system can receive a set of security vulnerabilities generated by a set of security tools incorporated with the CI/CD pipeline. The system further receives contextual data associated with the set of security vulnerabilities from a set of DevOps tools used by the CI/CD pipeline. Next, the system augments the set of security vulnerabilities with the received contextual data. The system next prioritizes the augmented security vulnerabilities to identify a subset of high-priority vulnerabilities within the set of security vulnerabilities. The system subsequently notifies the owners of the identified subset of high-priority vulnerabilities to cause the subset of high-priority vulnerabilities to be fixed by the owners.

Abstract: This disclosure describes techniques that include using an automatically trained machine learning system to generate a prediction. In one example, this disclosure describes a method comprising: based on a request for the prediction: training each respective machine learning (ML) model in a plurality of ML models to generate a respective training-phase prediction in a plurality of training-phase predictions; automatically determining a selected ML model in the plurality of ML models based on evaluation metrics for the plurality of ML; and applying the selected ML model to generate the prediction based on data collected from a network that includes a plurality of network devices.

Abstract: A variety of different graphical user interfaces are generated that when displayed provide a visual and interactive representation of one or more performance metrics associated with the operation of a computer network. The graphical user interfaces may be used to monitor the underlay computer network for a virtualization infrastructure, as one example. Aspects include grouping the servers of a computer network into a plurality of aggregates, each aggregate comprising one or more servers. A set of probes are configured that are issued by an agent of a server in one aggregate and sent through the computer network to one or more agents in the server(s) of a different aggregate. Responses and other measurements taken based on the issuance of the probes is gathered and analyzed to generate metrics that are then used to generate, at least in part, the information provided in the graphical user interfaces.

Abstract: This disclosure describes techniques that include using an automatically trained machine learning system to generate a prediction. In one example, this disclosure describes a method comprising: based on a request for the prediction: training each respective machine learning (ML) model in a plurality of ML models to generate a respective training-phase prediction in a plurality of training-phase predictions; automatically determining a selected ML model in the plurality of ML models based on evaluation metrics for the plurality of ML; and applying the selected ML model to generate the prediction based on data collected from a network that includes a plurality of network devices.

Abstract: This disclosure describes techniques that include collecting flow data associated with communications between network devices, and determining, based on the flow data, one or more virtual networks over which the communications are taking place. In one example, this disclosure describes a system configured to perform operations comprising: storing virtual network configuration information associated with a first virtual network and a second virtual network established within a network; collecting underlay flow data associated with communications between a first server and a second server, wherein each of the first server and the second server are implemented as bare metal servers; determining, based on the underlay flow data and the stored virtual network configuration information, that the first server and the second server have communicated over the first virtual network; and generating a user interface.

Abstract: A variety of different graphical user interfaces are generated that when displayed provide a visual and interactive representation of one or more performance metrics associated with the operation of a computer network. The graphical user interfaces may be used to monitor the underlay computer network for a virtualization infrastructure, as one example. Aspects include grouping the servers of a computer network into a plurality of aggregates, each aggregate comprising one or more servers. A set of probes are configured that are issued by an agent of a server in one aggregate and sent through the computer network to one or more agents in the server(s) of a different aggregate. Responses and other measurements taken based on the issuance of the probes is gathered and analyzed to generate metrics that are then used to generate, at least in part, the information provided in the graphical user interfaces.

Abstract: An example control plane that is executed on one or more processors in a distributed computing system is configured to receive an indication of a node to be onboarded into the distributed computing system, wherein the node comprises one of a compute node or a network device node, to discover one or more compute resources or network device resources that are associated with the node, and to assign, based on the discovery, the node to a collector that is executed in the distributed computing system, wherein the collector is configured to collect real-time telemetry data for the node during operation of the node. The control plane is further configured to receive, from the collector, the real-time telemetry data for the node that is collected by the collector, and to output, for display, a visual representation of the real-time telemetry data for the node.