US 12,288,137 B2
Performance prediction using dynamic model correlation
Nigel Slinger, Los Gatos, CA (US); Wenjie Zhu, Dublin (IE); Roxanne Kallman, Ham Lake, MN (US); Catherine Drummond, Morgan Hill, CA (US); and John Flournoy, Lago Vista, TX (US)
Assigned to BMC Software, Inc., Houston, TX (US)
Filed by BMC Software, Inc., Houston, TX (US)
Filed on Oct. 30, 2020, as Appl. No. 16/949,477.
Claims priority of provisional application 62/704,966, filed on Jun. 4, 2020.
Prior Publication US 2021/0383271 A1, Dec. 9, 2021
Int. Cl. G06N 20/00 (2019.01)
CPC G06N 20/00 (2019.01) 14 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:
receive a data stream of performance metrics characterizing a technology landscape, the performance metrics being a subset of available performance metrics;
convert model description data describing a plurality of performance prediction models into a model control file including consumable code that indexes the plurality of performance prediction models with respect to the available performance metrics and with respect to at least one correlation between two or more of the available performance metrics;
execute the model control file to select, from the plurality of performance prediction models and based on the performance metrics and on the at least one correlation, a subset of performance prediction models;
combine the subset of performance prediction models into a composite prediction model;
load the composite prediction model into a model processor for scoring against the data stream of performance metrics to obtain a performance prediction for the technology landscape based thereon;
determine, from the data stream of performance metrics, a context change within the technology landscape that results in an updated data stream of performance metrics;
execute the model control file to select an updated subset of performance predication models from the plurality of performance prediction models, based on the context change and on the at least one correlation, including adding at least one of the plurality of performance prediction models to the subset of performance prediction models and/or removing at least one of the subset of performance prediction models;
update the composite prediction model within the model processor with an updated composite prediction model that includes the updated subset of performance prediction models; and
load the updated composite prediction model into the model processor for scoring against the updated data stream of performance metrics to obtain an updated performance prediction for the technology landscape based thereon.