MemSweeper: virtualizing cluster memory management for high memory utilization and isolation

Abstract

Memory caches are critical components of modern web services that improve response times and reduce the load on backend databases. In multi-tenant clouds, several instances of caches compete for memory. The current state-of-the-art is to statically allocate memory for cache instances (e.g., based on cost-tier) but such allocation tends to be sub-optimal as memory demands of instances often vary with time and not known apriori. We propose MemSweeper, which dynamically manages memory between cache instances. MemSweeper uses a novel, score-based metric and an associated algorithm to identify cache instances whose working sets fit well within their allocated memory and thus can relinquish a portion of the memory without suffering appreciable loss in their hit rates. Using a combination of synthetic and production traces on a real implementation, we show that MemSweeper achieves 74% improvement (on average) in the miss rate of critical tenants without degrading the performance of other tenants.