Dynamic performance profiling of cloud caches

Abstract

In-memory object caches, such as memcached, are critical to the success of popular web sites, such as Facebook [3], by reducing database load and improving scalability [2]. The prominence of caches implies that configuring their ideal memory size has the potential for significant savings on computation resources and energy costs, but unfortunately cache configuration is poorly understood. The modern practice of manually tweaking live caching systems takes significant effort and may both increase the variance for client request latencies and impose high load on the database backend.