Self-Optimizing Caches | SNIA

Abstract

Caches in modern storage systems lack the ability to adapt automatically and optimize for dynamic workload mixes. Despite the potential for huge improvements in cost, performance, and predictability, such adaptability is extremely challenging, due to inherently complex, non-linear, and workload-dependent behavior. Even when manually-tunable controls are provided to support dynamic cache sizing, partitioning, and parameter tuning, administrators simply don’t have the information required to make good decisions. In this talk, we will present an overview of the significant opportunity for self-optimizing caches by examining several examples from production systems. We will review recently-published research in this area, including robust, general methods for efficient cache modeling. Optimizations that leverage these models promise to improve the performance of most workloads and cache policies automatically.