Large-scale in-memory object caches such as memcached are widely used to accelerate popular web sites and to reduce burden on backend databases. Yet current cache systems give cache operators limited information on what resources are required to optimally accommodate the present workload. This paper focuses on a key question for cache operators: how much total memory should be allocated to the in-memory cache tier to achieve desired performance? We present our Mimir system: a lightweight online profiler that hooks into the replacement policy of each cache server and produces graphs of the overall cache hit rate as a function of memory size. The profiler enables cache operators to dynamically project the cost and performance impact from adding or removing memory resources within a distributed in-memory cache, allowing "what-if" questions about cache performance to be answered without laborious offline tuning. Internally, Mimir uses a novel lock-free algorithm and lookup filters for quickly and dynamically estimating hit rate of LRU caches. Running Mimir as a profiler requires minimal changes to the cache server, thanks to a lean API. Our experiments show that Mimir produces dynamic hit rate curves with over 98% accuracy and 2--5% overhead on request latency and throughput when Mimir is run in tandem with memcached, suggesting online cache profiling can be a practical tool for improving provisioning of large caches.
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