Root cause analysis of performance problems

• US 10,210,189 B2
• Filed: 02/28/2018
• Issued: 02/19/2019
• Est. Priority Date: 01/06/2017
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Anand, Manish, Khan, Pathan Niyazulla, Singh, Sweta
• Primary Examiner(s): Hu, Jensen
• Application Number:
  US15/907,353
• Publication Number:
  US 20180196837A1
• Time in Patent Office: 356 Days
• CPC Class Codes: G06F 11/079 Root cause analysis, i.e. error or fault diagnosis in a hardware test environment G06F11/22; in a software test environment G06F11/36
• CPC Class Codes: G06F 11/3409 for performance assessment
• CPC Class Codes: G06F 11/3452 Performance evaluation by statistical analysis
• CPC Class Codes: G06F 16/217 Database tuning G06F16/2282 takes precedence; database performance monitoring G06F11/3409
• CPC Class Codes: G06F 2201/80 Database-specific techniques

Abstract

A method for analyzing performance of a computer system where at a plurality of time intervals, a set of KPIs of a database and database performance outputs are monitored. A database performance value is calculated based on the monitored KPIs and one or more database performance outputs. In response to determining the database performance value is below a first threshold, a KPI correlation coefficient is calculated. The calculated KPI correlation coefficient with the greatest value is selected as a target KPI. In response to determining that a count of functions of the target KPI meets a second threshold, a dynamic tabular representation of the determined target KPI is generated. A correlation matrix is generated based on the generated tabular representation and the plurality of time intervals and a first set of target functions are identified based on the generated correlation matrix.

Claims

1. A method for identifying a computer performance bottleneck by analyzing performance of a computer system based on key performance indicators (KPIs), the method comprising:

at a plurality of time intervals, monitoring a set of KPIs of a database and database performance outputs, wherein a KPI is associated with execution of one or more functions;

calculating a database performance value based on the monitored KPIs and one or more database performance outputs;

in response to determining the database performance value is below a first threshold, calculating a KPI correlation coefficient representing a correlation between the set of KPIs and corresponding database performance values;

selecting the calculated KPI correlation coefficient with the greatest value as a target KPI;

in response to determining that a count of functions of the target KPI meets a second threshold, generating a dynamic tabular representation of the determined target KPI, wherein the columns represent KPIs and the rows represent functions;

generating a correlation matrix based on the generated tabular representation and the plurality of time intervals;

identifying a first set of target functions, where in the target functions are SQL functions associated with the correlation coefficient with the greatest value, based on the generated correlation matrix;

determine a time spent per function value for each target functions of the set of target functions;

in response to the determined time spent per function value being above a third threshold, generating a dynamic tabular representation of the target KPI, wherein the tabular representation includes columns and rows, wherein the columns represent one or more KPIs and the rows represent SQL functions;

communicating, via a display on a user device, the generated dynamic tabular representation of the target KPI to a user;

in response to receiving a user input selection via the user device, modifying the generated dynamic tabular representation of the target KPI, wherein modifying the generated dynamic tabular representation of the target KPI comprises applying a weight to the KPI correlation coefficient of the target KPI; and

identify a performance bottleneck based on the modified tabular representation of the target KPI and received user input.