Compass: optimizing the migration cost vs. application performance tradeoff

We investigate methodologies for placement and migration of logical data stores in virtualized storage systems leading to optimum system configuration in a dynamic workload scenario. The aim is to optimize the tradeoff between the performance or operational cost improvement resulting from changes in store placement, and the cost imposed by the involved data migration step. We propose a unified economic utility based framework in which the tradeoff can be formulated as a utility maximization problem where the utility of a configuration is defined as the difference between the benefit of a configuration and the cost of moving to the configuration. We present a storage management middleware framework and architecture Compass that allows systems designers to plug-in different placement as well as migration techniques for estimation of utilities associated with different configurations. The biggest obstacle in optimizing the placement benefit and migration cost tradeoff is the exponential number of possible configurations that one may have to evaluate. We present algorithms that explore the configuration space efficiently and compute a candidate set of configurations that optimize this cost-benefit tradeoff. Our algorithms have many desirable properties including local optimality. Comprehensive experimental studies demonstrate the efficacy of the proposed framework and exploration algorithms, as our algorithms outperform migration cost-oblivious placement strategies by up to 40% on real OLTP traces for many settings.