In-memory caches play a critical role in storage environments by reducing data access latencies and loads on backend data stores. A cache's eviction policy significantly impacts its attained miss ratio, and recent modeling techniques allow for efficient evaluation of different eviction policies at runtime. However, modern in-memory caches lack the ability to switch between eviction policies at runtime, except for Redis that can only switch between LRU and LFU. We present PaperCache, an in-memory cache capable of switching between multiple different eviction policies at runtime. Our evaluation shows that immediately after an eviction policy switch, PaperCache's behavior closely mirrors that of a cache implementing the target policy exactly (with a miss ratio typically within 1%) for a short period of time, after which PaperCache's behavior is fully inline with an exact policy implementation. Further, PaperCache is able to periodically and automatically switch to the policy exhibiting the lowest miss ratio, reducing the overall miss ratio by up to 48.5%.

In-memory caches play a pivotal role in optimizing distributed systems by significantly reducing query response times. Correctly sizing these caches is critical, especially considering that prominent organizations use terabytes and even petabytes of DRAM for these caches. The Miss Ratio Curve (MRC) and Working Set Size (WSS) are the most widely used tools for sizing these caches.