DLIRS: Improving Low Inter-Reference Recency Set Cache Replacement Policy with Dynamics

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Presented at Systor2018Haifa 2018 by

Cong Li

As one of the state-of-the-art policies for buffer cache replacement, Low Inter-Reference Recency Set (LIRS) uses InterReference Recency (IRR) to predict future access behaviors of blocks. With a static allocation of most cache space to low IRR blocks, it does not perform well in some LRU-friendly workloads. Inspired by the idea of dynamic cache space partitioning from another state-of-the-art policy, Adaptive Replacement Cache (ARC), we propose a new Dynamic LIRS (DLIRS) policy. The new policy uses a simple mechanism to perform an approximated online estimation on how well IRR predicts future access behaviors, and then dynamically adapts the space allocation of low IRR blocks against high IRR blocks. Experiments are performed on traces from theUMass Trace Repository as well as a synthetic trace drawn from a stack depth distribution. While sometimes LIRS outperforms ARC with a significant margin and sometimes viceversa, the new DLIRS policy consistently performs close to the winner between ARC and LIRS in all the cases.