Automatic identification of workloads contributing to system performance degradation using machine learning techniques
US11062173B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Oct 22, 2019 |
| Grant date | Jul 13, 2021 |
| Priority date | — |
| Expiry date | Dec 2, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F2218/12
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Methods, apparatus, and processor-readable storage media for automatic identification of workloads contributing to system performance degradation are provided herein. An example computer-implemented method includes obtaining, in connection with a system exhibiting performance degradation, a primary time series and a set of multiple candidate time series; calculating, using machine learning, similarity measurements between the primary time series and each time series in the set; for each measurement, assigning weights to the time series based on similarity to the primary time series relative to the other time series in the set; generating, for each time series in the set, a similarity score based on the weights assigned across the similarity measurements; and outputting, based on the similarity scores, identification of a candidate time series for use in automated actions.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.