Reconciling Cost and Performance Objectives for Elastic Web Caches

Web and service applications are generally I/O bound and follow a Zipf-like request distribution, ushering in potential for significant latency reduction by caching and reusing results. However, such web caches require manual resource allocation, and when deployed in the cloud, costs may further complicate the provisioning process. We propose a fully autonomous, self-scaling, and cost-aware cloud cache with the objective of accelerating data-intensive applications. Our system, which is distributed over multiple cloud nodes, intelligently provisions resources at runtime based on user's cost and performance expectations, while abstracting the various low-level decisions regarding efficient cloud resource management and data placement within the cloud from the user. Our prediction model lends the system the capability to auto-configure the optimal resource requirement to automatically scale itself up (or down) to accommodate demand peaks while staying within certain cost constraints while fulfilling the performance expectations. Our evaluation shows a 5.5 time speedup for a typical web workload, while staying under cost constraints.