Elastic Resource Allocation For A Cloud-Based Web Caching System

Web and service applications are generally I/O bound and follow a highly skewed 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 away the various low-level decisions regarding efficient cloud resource management and data placement within the cloud. 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 and fulfilling the performance expectations.