MuMs: Energy-Aware VM Selection Scheme for Cloud Data Center

Abstract

The energy consumption of data centers has been increasing continuously during the last years due to the rising demands of computational power especially in current Grid- and Cloud Computing systems, which directly influence the increment in operational costs as well as carbon dioxide (CO2) emission. To reduce energy consumption within the cloud data center, it required energy-aware virtual machines (VMs) selection algorithms for VM consolidation at time host detected underloaded and overloaded and after allocating resources to all VMs from the underloaded hosts required to turn into energy saving-mode. In this paper, we propose energy-aware dynamic VM selection algorithms for consolidating the VMs from overloaded or underloaded host for minimising the total energy consumption and maximise the Quality of Service (QoS) include the reduction of service level agreements (SLAs) violation. To validate our scheme, we implemented it using CloudSim simulator and conducted simulations on the 10 different day's real workloads trace, which provided by the PlanetLab.