Measuring the capability of AIOps systems deployed in computing environments

    公开(公告)号:US12164841B2

    公开(公告)日:2024-12-10

    申请号:US17447448

    申请日:2021-09-13

    Abstract: An aspect of the present disclosure facilitates measuring the capability of AIOps (Artificial Intelligence for IT operations) systems deployed in computing environments. In one embodiment, a first simulation of a target AIOps system is run using a first historical input set having a corresponding first actual output set of a first AIOps system different from the target AIOps system. A second simulation of a reference AIOps system is run using a second historical input set having a corresponding second actual output set of the same first AIOps system. A first and second accuracy scores are determined based on outputs of the first and second simulations and the corresponding first and second actual output sets. An enablement score representing a measure of the capability (in terms of accuracy of prediction) of the target AIOps system is generated based on the first accuracy score and the second accuracy score.

    MEASURING THE CAPABILITY OF AIOPS SYSTEMS DEPLOYED IN COMPUTING ENVIRONMENTS

    公开(公告)号:US20230008225A1

    公开(公告)日:2023-01-12

    申请号:US17447448

    申请日:2021-09-13

    Abstract: An aspect of the present disclosure facilitates measuring the capability of AIOps (Artificial Intelligence for IT operations) systems deployed in computing environments. In one embodiment, a first simulation of a target AIOps system is run using a first historical input set having a corresponding first actual output set of a first AIOps system different from the target AIOps system. A second simulation of a reference AIOps system is run using a second historical input set having a corresponding second actual output set of the same first AIOps system. A first and second accuracy scores are determined based on outputs of the first and second simulations and the corresponding first and second actual output sets. An enablement score representing a measure of the capability (in terms of accuracy of prediction) of the target AIOps system is generated based on the first accuracy score and the second accuracy score.

    FORECASTING OF RESOURCE REQUIREMENTS FOR COMPONENTS OF SOFTWARE APPLICATIONS

    公开(公告)号:US20230176920A1

    公开(公告)日:2023-06-08

    申请号:US18062033

    申请日:2022-12-06

    CPC classification number: G06F9/5055 G06F9/5038 G06F2209/5019

    Abstract: An aspect of the present disclosure is directed to forecasting resource requirements for components of software applications. In one embodiment, a system constructs a component graph of components deployed in a computing environment, the component graph indicating for each component, a corresponding subset of components that are invoked by the component and a corresponding distribution of component workloads received at the component to the subset of components. Upon receiving data indicating an entry workload expected to be received in a future duration at one or more entry components, the system estimates by traversing the component graph, a component workload, corresponding to the entry workload, expected to be received in the future duration at a first component and determines resource requirements for the first component based on the estimated component workload.

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