Energy-aware Virtual Machine Management Optimization in Clouds

Cloud computing provides a kind of dynamic and scalable service on demand. However, clouds consume huge amountsof electrical energy. Meanwhile, delivering the negotiated QoS defined as Service Level Agreement (SLA) to users is necessary. A virtual machine placement strategy based on the equilibrium between energy and SLA is proposed. Aiming at dynamical changes of application workloads, an adaptive placement strategy RLWR based on robust local weight regression is presented, which decides the overload time of hosts dynamically according to the historical resource occupation of application workload. Then, two virtual machine migration selection algorithms, MPM and MNM are presented.The migrated virtual machines are deployed using bin-packing algorithm PBFDH. Contrasting to static algorithms such as STH, MPA and DVFS, virtual machines are not only deployed on fewer hosts in our algorithm, which promotes energy efficiency, but the load prediction can bring high-reliable QoS delivery and avoid overmuch SLA violations. Experimental results show that our strategy has an obvious effect on decreasing SLA violation under ensuring energy efficiency.