Cloud affinity based on evaluation of static and dynamic workload characteristics
US11928513B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 28, 2022 |
| Grant date | Mar 12, 2024 |
| Priority date | — |
| Expiry date | Dec 28, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F2209/5019
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Mechanisms are provided for scheduling a workload in a cloud computing system. A cloud affinity factor (CAF) computer model is trained, via a machine learning process based on a training dataset comprising static characteristics of a workload binary for a workload, and dynamic characteristics corresponding to historical performance data for the workload, such that the trained CAF computer model predicts a performance classification for a given workload binary. The trained CAF computer model processes a new workload to generate a performance classification for the new workload. Cloud affinity factor(s) are generated based on the performance classification for the new workload. Node affinity and dispatch rule(s) are applied to the cloud affinity factor(s) to select one or more nodes of the cloud computing system to which to dispatch the workload. The workload is then scheduled on the selected one or more nodes.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.