Computing task scheduling based on an intrusiveness metric
AssigneeHewlett-Packard Development Company, L.P.
InventorsChristian Makaya, Madhu Sudan Athreya
Life of this patent
Estimating the term…
Abstract
Examples of computing task scheduling based on an intrusiveness metric are described. In an example, an intrusiveness metric that indicates an impact of a computing task on performance of a computing device may be determined with an intrusiveness machine learning model. The intrusiveness metric may be sent to a scheduler device to determine distribution of additional computing tasks according to a scheduling machine learning model.
Claims19
A method by a computing device, comprising:
- determining an intrusiveness metric that indicates an impact of a computing task on performance of a computing device, the intrusiveness metric being determined with an intrusiveness machine learning model;
- and
- sending the intrusiveness metric to a scheduler device to determine distribution of additional computing tasks according to a scheduling machine learning model, wherein the scheduler device further receives additional intrusiveness metrics from additional computing devices and determines the distribution of the additional computing tasks also according to the intrusiveness metric and the additional intrusive metrics, and wherein the additional intrusiveness metrics are determined by additional intrusiveness machine learning models on the additional computing devices.
A scheduler device, comprising:
- a memory;
- a processor coupled to the memory, wherein the processor is to:
- receive, from a plurality of computing devices, intrusiveness metrics that indicate an impact of computing tasks on performance of the computing devices, the intrusiveness metrics being determined by an intrusiveness machine learning model on the computing devices;
- and
- determine distribution of additional computing tasks according to a scheduling machine learning model and the intrusiveness metrics.
A non-transitory tangible computer-readable medium storing executable code, comprising:
- code to cause a processor to receive a machine learning training task from a scheduler device;
- code to cause the processor to determine an intrusiveness metric using an intrusiveness machine learning model in response to performing the machine learning training task;
- and
- code to cause the processor to report the intrusiveness metric to the scheduler device, wherein, the scheduler device further receives additional intrusiveness metrics determined by additional intrusiveness machine learning models on additional computing devices, and determines distribution of additional computing tasks according to a scheduling machine learning model, the intrusiveness metric, and the additional intrusiveness metric.
Dependencies are inferred from the claim text.