ESCAPe: Elastic Caching For Big Data Systems

In recent years, in-memory cache systems have been commonly utilized to help maintain low application response times, compared to traditional relational databases, where the data items are stored in disk drives. Although cache memory systems offer improved performance, running everything in memory might not be cost-effective. In this paper, we present ESCAPe, an elastic high throughput and low latency key-value in-memory cache system. Unlike existing schemes, ESCAPe offers an elastic mechanism that proactively adds or removes nodes to scale-down or scale-up to meet fluctuating application demands, and incorporates a dynamic redistribution scheme that prioritizes the distribution of the keys at the nodes, while keeping the overhead cost as low as possible. We have evaluated our approach in a real cluster, using ESCAPe as the memcache system for Web Applications using different workload traces and comparing our approach with state of the art schemes. Our results illustrate that ESCAPe is able to select the most useful items to keep in memory, significantly reducing the end-to-end latency experienced by the applications.