Adaptive Software Cache Management

Developing a silver bullet software cache management policy is a daunting task due to the variety of potential workloads. In this paper, we investigate an adaptivity mechanism for software cache management schemes which offer tuning parameters targeted at the frequency vs. recency bias in the workload. The goal is automatic tuning of the parameters for best performance based on the workload without any manual intervention. We study two approaches for this problem, a hill climbing solution and an indicator based solution. In hill climbing, we repeatedly reconfigure the system hoping to find its best setting. In the indicator approach, we estimate the workloads' frequency vs. recency bias and adjust the parameters accordingly in a single swoop. We apply these adaptive mechanisms to two recent software management schemes. We perform an extensive evaluation of the schemes and adaptation mechanisms over a large selection of workloads with varying characteristics. With these, we derive a parameterless software cache management policy that is competitive for all tested workloads.