Machine learning to improve caching efficiency in a storage system
US11693570B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Apr 29, 2021 |
| Grant date | Jul 4, 2023 |
| Priority date | — |
| Expiry date | Apr 29, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F2212/7205
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A system and method improve caching efficiency in a data storage system by performing machine learning processes on metadata relating to extents of data blocks, rather than individual blocks themselves. Thus, once the storage devices are divided into extents, various metadata regarding access to the blocks within each extent are aggregated, and per-extent features are extracted. These features are used to train a data regression model that is subsequently used to infer a most likely “hotness” value for each extent at a future time. These predicted values, which may be further classified as e.g. “hot”, “warm”, and “cold” using thresholds, are used to implement the cache replacement policy. Embodiments scale to large and multi-layered caches, and may avoid common caching problems like thrashing, by adjusting the extent size. Policy goal functions may be optimized by dynamically adjusting the classification thresholds.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.