Adaptive Performance-Aware Distributed Memory Caching

Distributed in-memory caching systems such as memcached have become crucial for improving the performance 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 intelligently decides how to scale a distributed caching system 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 system 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.