Framework for automated application-to-network root cause analysis

    公开(公告)号:US12199813B2

    公开(公告)日:2025-01-14

    申请号:US18345422

    申请日:2023-06-30

    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.

    DISTRIBUTED APPLICATION CALL PATH PERFORMANCE ANALYSIS

    公开(公告)号:US20250112851A1

    公开(公告)日:2025-04-03

    申请号:US18478260

    申请日:2023-09-29

    Abstract: In general, techniques are described for managing a distributed application based on call paths among the multiple services of the distributed application that traverse underlying network infrastructure. In an example, a method comprises determining, by a computing system, and for a distributed application implemented with a plurality of services, a call path from an entry endpoint service of the plurality of services to a terminating endpoint service of the plurality of services; determining, by the computing system, a corresponding network path for each pair of adjacent services from a plurality of pairs of services that communicate for the call path; and based on a performance indicator for a network device of the corresponding network path meeting a threshold, performing, by the computing system, one or more of: reconfiguring the network; or redeploying one of the plurality of services to a different compute node of the compute nodes.

    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.

    MACHINE LEARNING PIPELINE FOR PREDICTIONS REGARDING A NETWORK

    公开(公告)号:US20230031889A1

    公开(公告)日:2023-02-02

    申请号:US17938895

    申请日:2022-10-07

    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.

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