Driving Cache Replacement with ML-based LeCaR

Abstract

Can machine learning (ML) be used to improve on existing cache replacement strategies? We propose a general framework called LeCaR that uses the ML technique of regret minimization to answer the question in the affirmative. Surprisingly, we show that the LeCaR framework outperforms A RC using only two fundamental eviction policies – LRU and LFU. We also show that the performance gap increases when the size of the available cache gets smaller relative to the size of the working set.