Learning Cache Replacement with Cacheus

Abstract

Recent advances in machine learning open up new and attractive approaches for solving classic problems in computing systems. For storage systems, cache replacement is one such problem because of its enormous impact on performance. We classify workloads as a composition of four workload primitive types—LFU-friendly, LRU-friendly, scan, and churn. We then design and evaluate CACHEUS, a new class of fully adaptive, machine-learned caching algorithms that utilize a combination of experts designed to address these workload primitive types. The experts used by CACHEUS include the state-of-the-art ARC, LIRS and LFU, and two new ones – SR-LRU, a scan-resistant version of LRU, and CR-LFU, a churn-resistant version of LFU. We evaluate CACHEUS using 17766 simulation experiments on a collection of 329 workloads run against 6 different cache configurations. Paired t-test analysis demonstrates that CACHEUS using the newly proposed lightweight experts, SR-LRU and CR-LFU, is the most consistently performing caching algorithm across a range of workloads and cache sizes. Furthermore, CACHEUS enables augmenting state-of-the-art algorithms (e.g., LIRS, ARC) by combining it with a complementary cache replacement algorithm (e.g., LFU) to better handle a wider variety of workload primitive types.