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

Systems for processing large scale analytical work- loads are increasingly moving from on-premise setups to on- demand configurations deployed on scalable cloud infrastruc- tures. 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