Resource tuning with usage forecasting

Patent metadata

Field	Value
Publication number	US-12288097-B2
Application number	US-202217649543-A
Country	US
Kind code	B2
Filing date	Jan 31, 2022
Priority date	Jan 31, 2022
Publication date	Apr 29, 2025
Grant date	Apr 29, 2025

Abstract

Described techniques determine performance metric values of a performance metric characterizing a performance of a system resource of an information technology (IT) system, and determine driver metric values of a driver metric characterizing an occurrence of an event that is at least partially external to the system resource. A correlation analysis may confirm a potential correlation between the performance metric values and the driver metric values as a correlation. A graph relating the performance metric to the driver metric may be generated. A plurality of extrapolation algorithms may be trained to obtain a plurality of trained extrapolation algorithms using a first subset of data points of the graph, and the plurality of trained extrapolation algorithms may be validated using a second subset of data points of the graph. A driver metric threshold corresponding to the performance metric threshold may be determined using a validated extrapolation algorithm.

First claim

What is claimed is:

1. A computer program product, the computer program product being tangibly embodied on a non-transitory computer-readable storage medium and comprising instructions that, when executed by at least one computing device, are configured to cause the at least one computing device to:
determine, as a first time series, performance metric values of a performance metric characterizing a performance of a system resource of an information technology (IT) system, the performance metric having a performance metric threshold at which the performance of the system resource degrades;
determine, as a second time series, driver metric values of a driver metric characterizing an occurrence of an event that is at least partially external to the system resource and having a potential correlation with the performance of the system resource;
perform a correlation analysis between the first time series and the second time series to confirm the potential correlation as a correlation;
identify correlated value pairs of the first time series and the second time series, each value pair occurring at a corresponding point in time, based on the correlation;
train a plurality of extrapolation algorithms to obtain a plurality of trained extrapolation algorithms using a first subset of the correlated value pairs;
validate the plurality of trained extrapolation algorithms to obtain a plurality of validated extrapolation algorithms using a second subset of the correlated value pairs;
select a validated extrapolation algorithm of the validated extrapolation algorithms; and
determine a driver metric threshold corresponding to the performance metric threshold, including using the validated extrapolation algorithm to extend a relationship between the performance metric values and the driver metric values until the performance metric threshold is met, to thereby determine the driver metric threshold corresponding to the performance metric threshold; and
tune the system resource to improve the performance metric threshold and thereby extend the driver metric threshold.

2. The computer program product of claim 1 wherein the instructions, when executed, are further configured to cause the at least one computing device to:
perform the correlation analysis including removing a first trend of the first time series and a second trend of the second time series.

3. The computer program product of claim 1 , wherein the instructions, when executed to perform the correlation analysis, are further configured to cause the at least one computing device to:
calculate an estimate of a Spearman correlation coefficient;
calculate a comparison of the estimate of the Spearman correlation coefficient and a coefficient threshold; and
confirm the potential correlation as the correlation based at least in part on the comparison.

4. The computer program product of claim 1 , wherein the instructions, when executed, are further configured to cause the at least one computing device to:
calculate a false positive error rate for the potential correlation;
calculate a comparison of the false positive error rate and a false positive error threshold for the potential correlation; and
confirm the potential correlation as the correlation based at least in part on the comparison.

5. The computer program product of claim 1 , wherein the instructions, when executed to generate the graph, are further configured to cause the at least one computing device to:
generate a graph in which the performance metric values are individually graphed against corresponding ones of the driver metric values to thereby illustrate the value pairs.

6. The computer program product of claim 1 , wherein the instructions, when executed, are further configured to cause the at least one computing device to:
iterate, for the driver metric, over a plurality of performance metrics that includes the performance metric, to thereby determine a plurality of performance metric thresholds and corresponding driver metric thresholds; and
identify a bottleneck performance metric of the plurality of performance metrics as corresponding to a smallest driver metric threshold of the plurality of driver metric thresholds.

7. The computer program product of claim 1 , wherein the instructions, when executed, are further configured to cause the at least one computing device to:
receive a hypothetical driver metric value;
project a hypothetical performance metric value, based on the hypothetical driver metric value and on the validated extrapolation algorithm;
determine that the hypothetical performance metric value exceeds the performance metric threshold;
tune the system resource, based on the exceeding; and
determine that an updated hypothetical performance metric value does not exceed the performance metric threshold with the adjusted capacity of the system resource, based on the hypothetical driver metric value and the validated extrapolation algorithm.

8. The computer program product of claim 1 , wherein the instructions, when executed, are further configured to cause the at least one computing device to:
train a plurality of prediction algorithms using a first subset of the driver metric values to obtain a plurality of trained prediction algorithms;
validate the plurality of trained prediction algorithms using a second subset of the driver metric values to obtain a plurality of validated prediction algorithms;
select a validated prediction algorithm of the validated prediction algorithms as exceeding a predictability threshold; and
predict future driver metric values, using the validated prediction algorithm and the driver metric values.

9. The computer program product of claim 8 , wherein the instructions, when executed, are further configured to cause the at least one computing device to:
predict a point in time at which the driver metric will reach the driver metric threshold, using the validated prediction algorithm.

10. A computer-implemented method, the method comprising:
determining, as a first time series, performance metric values of a performance metric characterizing a performance of a system resource of an information technology (IT) system, the performance metric having a performance metric threshold at which the performance of the system resource degrades;
determining, as a second time series, driver metric values of a driver metric characterizing an occurrence of an event that is at least partially external to the system resource and having a potential correlation with the performance of the system resource;
performing a correlation analysis to confirm the potential correlation as a correlation;
identifying correlated value pairs of the first time series and the second time series, each value pair occurring at a corresponding point in time, based on the correlation;
training a plurality of extrapolation algorithms to obtain a plurality of trained extrapolation algorithms using a first subset of the correlated value pairs;
validating the plurality of trained extrapolation algorithms to obtain a plurality of validated extrapolation algorithms using a second subset of the correlated value pairs;
selecting a validated extrapolation algorithm of the validated extrapolation algorithms; and
determining a driver metric threshold corresponding to the performance metric threshold, including using the validated extrapolation algorithm to extend a relationship between the performance metric values and the driver metric values until the performance metric threshold is met, to thereby determine the driver metric threshold corresponding to the performance metric threshold; and
tuning the system resource to improve the performance metric threshold and thereby exten

Assignees

• Bmc Software Inc
• Bmc Helix Inc

Inventors

• Front Yaron
• De Stefano Michele
• Bertoli Marco
• Sivasubramanian Jeyashree
• Padmawar Komal
• Yavin Nir

Classifications

• G06F11/3409 Primary

  for performance assessment · CPC title

• G06N20/00

  Machine learning · CPC title

• G06F9/5011

  the resources being hardware resources other than CPUs, Servers and Terminals · CPC title

• G06F2209/5019

  Workload prediction · CPC title

• G06F11/3447

  Performance evaluation by modeling · CPC title