Practical Online Cache Analysis and Optimization

The benefits of storage caches are notoriously difficult to model and control, varying widely by workload, and exhibiting complex, nonlinear behaviors. However, recent advances make it possible to analyze and optimize high-performance storage caches using lightweight, continuously-updated miss ratio curves (MRCs). Previously relegated to offline modeling, MRCs can now be computed so inexpensively that they are practical for dynamic, online cache management, even in the most demanding environments. After reviewing the history and evolution of MRC algorithms, we will examine new opportunities afforded by recent techniques. MRCs capture valuable information about locality that can be leveraged to guide efficient cache sizing, allocation, and partitioning, in order to support diverse goals such as improving performance, isolation, and quality of service. We will also describe how multiple MRCs can be used to track different alternatives at various timescales, enabling online tuning of cache parameters and policies. Learning Objectives: Storage cache modeling and analysis Efficient cache sizing, allocation, and partitioning Online tuning of commercial storage cache parameters and policies