Energy-efficient resource allocation and provisioning for in-memory database clusters

Systems for processing large scale analytical workloads are increasingly moving from on-premise setups to on-demand configurations deployed on scalable cloud infrastructures. To reduce the cost of such infrastructures, existing research focuses on developing novel methods for workload and server consolidation. In this paper, we combine analytical modeling and non-linear optimization to help cloud providers increase the energy-efficiency of in-memory database clusters in cloud environments. We model this scenario as a multi-dimensional bin-packing problem and propose a new approach based on a hybrid genetic algorithm that efficiently handles resource allocation and server assignment for a given set of in-memory databases. Our trace-driven evaluation is based on measurements from an SAP HANA in-memory system and indicates improvements between 6% and 32% over the popular best-fit decreasing heuristic.