FRAMEWORK FOR AUTOMATED APPLICATION-TO-NETWORK ROOT CAUSE ANALYSIS

    公开(公告)号:US20240007342A1

    公开(公告)日:2024-01-04

    申请号:US18345422

    申请日:2023-06-30

    CPC classification number: H04L41/0631 H04L41/16

    Abstract: A computing system comprising a memory and processing circuitry may perform the techniques. The memory may store time series data comprising measurements of one or more performance indicators. The processing circuitry may determine, based on the time series data, an anomaly in the performance of the network system, and create, based on the time series data, a knowledge graph. The processing circuitry may determine, in response to detecting the anomaly, and based on the knowledge graph and a machine learning (ML) model trained with previous time series data, a causality graph. The processing circuitry may determine a weighting for each edge in the causality graph, determine, based on the edges in the causality graph, a candidate root cause associated with the anomalies, and determine a ranking of the candidate root cause based on the weighting. The analysis framework system may output at least a portion of the ranking.

    APPLICATION-AWARE ACTIVE MEASUREMENT FOR MONITORING NETWORK HEALTH

    公开(公告)号:US20250150327A1

    公开(公告)日:2025-05-08

    申请号:US19018663

    申请日:2025-01-13

    Abstract: In general, this disclosure describes techniques that enable a network system to perform application-aware active measurement for monitoring network health. The network system includes memory that stores a topology graph for a network. The network system includes processing circuitry that may receive an identifier associated with an application utilizing the network for communications, and determine, based on the topology graph and the identifier, a subgraph of the topology graph based on a location, in the topology graph, of a node representing a compute node that is a host of the application. The processing circuitry may next determine, based on the subgraph, a probe module to measure performance metrics associated with the application, and for the probe module, generate configuration data corresponding to the probe module. The processing circuitry may output, to the probe module, the configuration data.

    GRAPH ANALYTICS ENGINE FOR APPLICATION-TO-NETWORK TROUBLESHOOTING

    公开(公告)号:US20250150326A1

    公开(公告)日:2025-05-08

    申请号:US19018627

    申请日:2025-01-13

    Abstract: A computing device may implement the techniques described in this disclosure. The computing device may include processing circuitry configured to execute an analysis framework system, and memory configured to store time series data. The analysis framework system may create, based on the time series data, a knowledge graph comprising a plurality of first nodes in the network system referenced in the time series data interconnected by edges. The analysis framework system may cause a graph analytics service of the analysis framework system to receive a graph analysis request comprising a request to determine a fault propagation path, a request to determine changes in the knowledge graph, a request to determine an impact of an emulated fault, or a request to determine an application-to-network path. The analysis framework system may also cause the graph analytics service to determine a response to the graph analysis request, and output the response.

    INTELLIGENT FIREWALL FLOW CREATOR
    14.
    发明公开

    公开(公告)号:US20240179126A1

    公开(公告)日:2024-05-30

    申请号:US18472042

    申请日:2023-09-21

    CPC classification number: H04L63/0263 H04L41/16 H04L63/0236

    Abstract: Example systems, methods, and storage media are described. An example network system includes processing circuitry and one or more memories coupled to the processing circuitry. The one or more memories are configured to store instructions which, when executed by the processing circuitry, cause the network system to obtain telemetry data, the telemetry data comprising indications of creations of instances of a flow. The instructions cause the network system to, based on the indications of the creations of the instances of the flow, determine a pattern of creation of the instances of the flow. The instructions cause the network system to, based on the pattern of creation of the instances of the flow, generate an action entry in a policy table for a particular instance of the flow prior to receiving a first packet of the particular instance of the flow.

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