Driving cache replacement with ML-based LeCaR Conference

cited authors

  • Vietri, G; Rodriguez, LV; Martinez, WA; Lyons, S; Liu, J; Rangaswami, R; Zhao, M; Narasimhan, G

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. We show that the LeCaR framework outperforms ARC using only two fundamental eviction policies, LRU and LFU, by more than 18x when the cache size is small relative to the size of the working set.

publication date

  • January 1, 2018