US 12,288,097 B2
Resource tuning with usage forecasting
Yaron Front, Kiryat Motzkin (IL); Michele De Stefano, Soncino (IT); Marco Bertoli, Trecate (IT); Jeyashree Sivasubramanian, Framingham, MA (US); Komal Padmawar, Pune (IN); and Nir Yavin, Kfar Szold (IL)
Assigned to BMC Helix, Inc., Houston, TX (US)
Filed by BMC Software, Inc., Houston, TX (US)
Filed on Jan. 31, 2022, as Appl. No. 17/649,543.
Prior Publication US 2023/0244535 A1, Aug. 3, 2023
Int. Cl. G06F 9/50 (2006.01); G06F 11/34 (2006.01); G06N 20/00 (2019.01)
CPC G06F 9/5033 (2013.01) [G06F 11/3409 (2013.01); G06N 20/00 (2019.01)] 19 Claims
OG exemplary drawing
 
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.