Abstract: Rapid growth of the demand for computational power has led to the creation of large-scale cloud computing data centers.The development of computing systems has always been focused on performance improvements driven by the demand of applications from consumer, scientific and business domains, but the ever increasing energy consumption of computing systems has started to limit further performance growth due to overwhelming energy consumption and carbon dioxide footprints. Hence, the goal of the computer system design has been shifted from performance improvements to power and energy efficiency. Data centers consume enormous amounts of electrical power resulting in high operational costs and carbon dioxide emissions. Moreover, modern Cloud computing environments have to provide high Quality of Service (QoS) for their customers resulting in the necessity to deal with power-performance trade-off. Reducing carbon emission by cloud computing data centers has emerged as one the dominant research topics both in industry and academia. The foremost objective of cloud service providers is to have a cost effective and energy efficient solution for allocating virtualized ICT resources to end-users’ application while meeting the QoS (Quality of Service) level as per SLA (Service Level Agreement).This paper presents a hybrid energy efficient resource allocation technique which combines predictive with reactive allocation techniques and accomplishes substantial improvements in: (a) meeting SLAs, (b) conserving energy, and (c)meeting static and dynamic resource allocation. In this paper we propose energy-aware allocation heuristics provision data center resources to client applications in a way that utilises the capability of VMs live migration to reallocate resources dynamically and improves energy efficiency of the data center, while delivering the negotiated Quality of Service (QoS). The basic idea is to use a heuristic that is consolidating and rearranging the allocation of resources in an energy efficient manner.

Keywords: Cloud Computing, Energy Efficiency, Resource Allocation, Virtualization, virtual machines, Data Center.