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：US10831648B2

公开(公告)日：2020-11-10

申请号：US16263363

申请日：2019-01-31

Applicant: Splunk Inc.

Inventor： Jian Zhang ,  Minghao Lu ,  Xiaolu Ye ,  Ning He

IPC: G06F9/44 ,  G06F11/36 ,  G06F9/50 ,  G06F17/11 ,  G06F9/48 ,  G06F9/54

Abstract: Systems and methods for testing a subject system with a software testing process are described. The system receives Boolean states responsive to repeatedly applying a first test case to a subject system. Each Boolean state signifies an outcome of an application of the first test case to a version of a first software feature over a span of time. The system identifies test case outcomes for the first test case that are adjacent in time and different and generates an intermittency value for the first test case. The system determines that the intermittency value for the first test case exceeds an intermittency threshold and alerts an engineering resource. Finally, the system repeats the above operations until the intermittency value for the first test case does not exceed the intermittency threshold.

公开(公告)号：US11409645B1

公开(公告)日：2022-08-09

申请号：US17017124

申请日：2020-09-10

Applicant: Splunk Inc.

Inventor： Jian Zhang ,  Minghao Lu ,  Xiaolu Ye ,  Ning He

IPC: G06F9/44 ,  G06F11/36 ,  G06F17/11 ,  G06F9/48 ,  G06F9/50 ,  G06F9/54

Abstract: Systems and methods for testing a subject system with a software testing process are described. The system receives Boolean states responsive to repeatedly applying a first test case to a subject system. Each Boolean state signifies an outcome of an application of the first test case to a version of a first software feature over a span of time. The system identifies test case outcomes for the first test case that are adjacent in time and different and generates an intermittency value for the first test case. The system determines that the intermittency value for the first test case exceeds an intermittency threshold and alerts an engineering resource. Finally, the system repeats the above operations until the intermittency value for the first test case does not exceed the intermittency threshold.

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.