Model-Driven Geo-Elasticity in Database Clouds

Tian Guo, Prashant J. Shenoy

Abstract: Motivated by the emergence of distributed clouds, we argue for the need for geo-elastic provisioning of application replicas to effectively handle temporal and spatial workload fluctuations seen by such applications. We present DBScale, a system that tracks geographic variations in the workload to dynamically provision database replicas at different cloud locations across the globe. Our geo-elastic provisioning approach comprises a regression-based model to infer the database query workload from observations of the spatially distributed front-end workload and a two-node open queueing network model to provision databases with both CPU and I/O-intensive query workloads. We implement a prototype of our DBScale system on Amazon EC2's distributed cloud. Our experiments with our prototype show up to a 66% improvement in response time when compared to local elasticity approaches.