Integrated Resiliency Planning in Storage Clouds

Storage clouds use economies of scale to host data for diverse enterprises. However, enterprises differ in the requirements for their data. In this work, we investigate the problem of resiliency or disaster recovery (DR) planning in a storage cloud. The resiliency requirements vary greatly between different enterprises and also between different datasets for the same enterprise. We present in this paper Resilient Storage Cloud Map (RSCMap), a generic cost-minimizing optimization framework for disaster recovery planning, where the cost function may be tailored to meet diverse objectives. We present fast algorithms that come up with a minimum cost DR plan, while meeting all the DR requirements associated with all the datasets hosted on the storage cloud. Our algorithms have strong theoretical properties: 2 factor approximation for bandwidth minimization and fixed parameter constant approximation for the general cost minimization problem. We perform a comprehensive experimental evaluation of RSCMap using models for a wide variety of replication solutions and show that RSCMap outperforms existing resiliency planning approaches.