Effective noise removal techniques for biased machine learning based optimizations in storage systems

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.