Adaptive performance-aware distributed memory caching

Distributed in-memory caching systems such as mem-cached have become crucial for improving the perfor-mance of web applications. However, memcached by itself does not control which node is responsible for each data object, and inefficient partitioning schemes can easily lead to load imbalances. Further, a statically sized memcached cluster can be insufficient or inefficient when demand rises and falls. In this paper we present an automated cache management system that both intel-ligently decides how to scale a distributed caching sys-tem and uses a new, adaptive partitioning algorithm that ensures that load is evenly distributed despite variations in object size and popularity. We have implemented an adaptive hashing system1 as a proxy and node control framework for memcached, and evaluate it on EC2 using a set of realistic benchmarks including database dumps and traces from Wikipedia.