US 12,405,728 B2
Workload management using a trained model
Mayukh Dutta, Karnataka (IN); Aesha Dhar Roy, Karnataka (IN); Manoj Srivatsav, Karnataka (IN); Ganesha Devadiga, Karnataka (IN); Geethanjali N. Rao, Karnataka (IN); Prasenjit Saha, Karnataka (IN); and Jharna Aggarwal, Karnataka (IN)
Assigned to Hewlett Packard Enterprise Development LP, Spring, TX (US)
Filed by Hewlett Packard Enterprise Development LP, Spring, TX (US)
Filed on May 29, 2024, as Appl. No. 18/677,326.
Application 18/677,326 is a continuation of application No. 17/303,883, filed on Jun. 9, 2021, granted, now 12,093,530.
Prior Publication US 2024/0319885 A1, Sep. 26, 2024
This patent is subject to a terminal disclaimer.
Int. Cl. G06F 3/06 (2006.01); G06N 20/00 (2019.01)
CPC G06F 3/0613 (2013.01) [G06F 3/0659 (2013.01); G06F 3/067 (2013.01); G06N 20/00 (2019.01)] 20 Claims
OG exemplary drawing
 
1. A non-transitory machine-readable storage medium comprising instructions that upon execution cause a system to:
create a training data set based on features of sample workloads, the training data set comprising labels associated with the features of the sample workloads, wherein the labels are based on load indicators generated in a computing environment relating to load conditions of the computing environment resulting from execution of the sample workloads;
group selected workloads into a plurality of workload clusters based on features of the selected workloads, wherein a workload cluster of the plurality of workload clusters comprises workloads grouped into the workload cluster according to a similarity criterion;
compute, using a model trained based on the training data set, parameters representing contributions of respective workload clusters of the plurality of workload clusters to a load condition in the computing environment, wherein the parameters comprise a first parameter representing a contribution of a first workload cluster to the load condition, and a second parameter representing a different contribution of a second workload cluster to the load condition, the first and second workload clusters being part of the plurality of workload clusters;
select a workload cluster of the plurality of workload clusters based on different values of the computed parameters; and
perform workload management in the computing environment based on the computed parameters, the workload management comprising restricting usage of a resource by a workload in the selected workload cluster.