Energy and QoS aware resource allocation for heterogeneous sustainable cloud datacenters

As the demand on Internet services such as cloud and mobile cloud services drastically increases recently, the energy consumption consumed by the cloud datacenters has become a burning topic. The deployment of renewable energy generators such as PhotoVoltaic (PV) and wind farms is an attractive candidate to reduce the carbon footprint and, achieve the sustainable cloud datacenters. However, current studies have focused on geographical load balancing of Virtual Machine (VM) requests to reduce the cost of brown energy usage, while most of them have ignored the heterogeneity of power consumption of each cloud datacenter and the incurred performance degradation by VM co-location. In this paper, we propose Evolutionary Energy Efficient Virtual Machine Allocation (EEE-VMA), a Genetic Algorithm (GA) based metaheuristic which supports a power heterogeneity aware VM request allocation of multiple sustainable cloud datacenters. This approach provides a novel metric called powerMark which diagnoses the power efficiency of each cloud datacenter in order to reduce the energy consumption of cloud datacenters more efficiently. Furthermore, performance degradation caused by VM co-location and bandwidth cost between cloud service users and cloud datacenters are considered to avoid the deterioration of Quality-of-Service (QoS) required by cloud service users by using our proposed cost model. Extensive experiments including real-world traces based simulation and the implementation of cloud testbed with a power measuring device are conducted to demonstrate the energy efficiency and performance assurance of the proposed EEE-VMA approach compared to the existing VM request allocation strategies. (C) 2016 Elsevier B.V. All rights reserved.