Effective noise removal techniques for biased machine learning based optimizations in storage systems
US12271596B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Aug 7, 2023 |
| Grant date | Apr 8, 2025 |
| Priority date | — |
| Expiry date | Aug 7, 2043 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F3/0685
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Techniques for performing effective noise removal for biased machine learning (ML) based optimizations in storage systems. The techniques include serving, by a storage system, an IO workload, identifying, using ML from among a plurality of storage objects subject to the IO workload, storage objects with low temperatures (e.g., cold storage objects) or likely to have low temperatures in the near future, and removing them from subsequent temperature forecasting analysis, effectively treating such cold storage objects as “noise.” The techniques further include performing the temperature forecasting analysis on remaining ones of the plurality of storage objects such as those with high temperatures (e.g., hot storage objects). In this way, temperature forecasting or prediction is performed, using ML, in a biased fashion over a relatively narrow spectrum of storage object temperatures, thereby improving tiering and data prefetching performance, reducing memory and processing overhead, and so on.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.