2015 IEEE International Conference on Autonomic Computing (ICAC) (2015)
July 7, 2015 to July 10, 2015
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICAC.2015.46
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 DB Scale, 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 DB Scale 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.
Servers, Cloud computing, Elasticity, Spatial databases, Mathematical model, Computational modeling
T. Guo and P. Shenoy, "Model-Driven Geo-Elasticity in Database Clouds," 2015 IEEE International Conference on Autonomic Computing (ICAC), Grenoble, France, 2015, pp. 61-70.