Driving Cache Replacement with ML-based LeCaR, Giuseppe Vietri, Liana V. Rodriguez, Wendy A. Martinez, Steven Lyons, Jason Liu, Raju Rangaswami, Giri Narasimhan, and Ming Zhao. In Proceedings of the 10th USENIX Workshop on Hot Topics in Storage and File Systems (HotStorage’18), July 2018. [to appear]
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 ARC 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.
Bibtex
@inproceedings{hotstorage18-lecar,
title = {Driving Cache Replacement with ML-based {LeCaR}},
author = {Giuseppe Vietri and Liana V. Rodriguez and Wendy A. Martinez and Steven Lyons and Jason Liu and Raju Rangaswami and Giri Narasimhan and Ming Zhao},
booktitle = {Proceedings of the 10th USENIX Workshop on Hot Topics in Storage and File Systems (HotStorage’18)},
month = {July},
year = {2018}
}