Automatic identification of workloads contributing to behavioral changes in storage systems using machine learning techniques
US11175829B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Oct 22, 2019 |
| Grant date | Nov 16, 2021 |
| Priority date | — |
| Expiry date | Jan 24, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/00
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Methods, apparatus, and processor-readable storage media for automatic identification of workloads contributing to behavioral changes in storage systems using machine learning techniques are provided herein. An example computer-implemented method includes obtaining a primary time series and a set of candidate time series; calculating, using machine learning techniques, similarity measurements between the primary time series and each candidate time series in the set; for each similarity measurement, assigning weights to the candidate time series based on similarity values; generating, for each candidate time series, a similarity score based on the assigned weights; automatically identifying, based on the similarity scores, a candidate time series as contributing to an anomaly exhibited in the primary time series; and outputting identifying information of the at least one identified candidate time series for use in one or more automated actions associated with the storage system.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.