Driving Cache Replacement with ML-based LeCaR

Authors: 

Giuseppe Vietri, Liana V. Rodriguez, Wendy A. Martinez, Steven Lyons, Jason Liu, and Raju Rangaswami, Florida International University; Ming Zhao, Arizona State University; Giri Narasimhan, Florida International University

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.

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BibTeX
@inproceedings {216890,
author = {Giuseppe Vietri and Liana V. Rodriguez and Wendy A. Martinez and Steven Lyons and Jason Liu and Raju Rangaswami and Ming Zhao and Giri Narasimhan},
title = {Driving Cache Replacement with ML-based LeCaR},
booktitle = {10th {USENIX} Workshop on Hot Topics in Storage and File Systems (HotStorage 18)},
year = {2018},
address = {Boston, MA},
url = {https://www.usenix.org/conference/hotstorage18/presentation/vietri},
publisher = {{USENIX} Association},
}