公开(公告)号：US20200342068A1

公开(公告)日：2020-10-29

申请号：US16455455

申请日：2019-06-27

Applicant: SPLUNK INC.

Inventor： Jie Cai ,  Yang Cao ,  Ning He ,  Bing Pei ,  Xiaolu Ye ,  Chong Yu ,  Aiping Zhang ,  Zhou Zhou

IPC: G06F17/50 ,  G06F9/455 ,  G06F8/77

Abstract: Computing devices, computer-readable storage media, and computer-implemented methods are disclosed for prediction of capacity. In a central tier, central-tier benchmark values are generated from benchmark testing performed on different test configurations in a reference execution environment. In a deployment tier, deployment-tier benchmark values are generated from benchmark testing performed on a baseline deployed configuration in many execution environments. A sizing model is learned from the central-tier benchmark values to predict execution platform requirements given a set of workload input parameters. A performance model is learned from the deployment-tier and the central-tier benchmark values to predict a performance delta value reflecting relative performance between a particular execution environment and the reference execution environment. The performance delta value is used to adjust predicted execution platform requirements to tailor the prediction to a particular execution environment. The predicted execution platform requirements can be deployed and tested to validate or tune the performance model.

公开(公告)号：US11995381B2

公开(公告)日：2024-05-28

申请号：US16455455

申请日：2019-06-27

Applicant: SPLUNK INC.

Inventor： Jie Cai ,  Yang Cao ,  Ning He ,  Bing Pei ,  Xiaolu Ye ,  Chong Yu ,  Aiping Zhang ,  Zhou Zhou

IPC: G06F30/20 ,  G06F8/77 ,  G06F9/455 ,  G06F11/34

CPC classification number: G06F30/20 ,  G06F8/77 ,  G06F9/455 ,  G06F11/3428 ,  G06F11/3447

Abstract: Computing devices, computer-readable storage media, and computer-implemented methods are disclosed for prediction of capacity. In a central tier, central-tier benchmark values are generated from benchmark testing performed on different test configurations in a reference execution environment. In a deployment tier, deployment-tier benchmark values are generated from benchmark testing performed on a baseline deployed configuration in many execution environments. A sizing model is learned from the central-tier benchmark values to predict execution platform requirements given a set of workload input parameters. A performance model is learned from the deployment-tier and the central-tier benchmark values to predict a performance delta value reflecting relative performance between a particular execution environment and the reference execution environment. The performance delta value is used to adjust predicted execution platform requirements to tailor the prediction to a particular execution environment. The predicted execution platform requirements can be deployed and tested to validate or tune the performance model.