Towards energy efficient resource management techniques in cloud computing

Abstract

Cloud computing shifts computing from local individual dedicated IT resources to distributed, virtual, elastic, scalable, high availability, reliable and multi-tenant resources as a service. In cloud computing there are two major actors namely cloud providers and cloud consumers. The Cloud Service Providers (CSP) points of view is to maximize revenue by achieving high resource utilization and minimize the cloud Data Center (DC) energy consumption while cloud consumers point of view is to minimize expenses while meeting their requirements. The CSP is looking for optimal resource allocation methods to minimize the energy consumption and looking for the state-of-the-art solution to ensure optimal energy consumption in cloud DC. The proposed Hybrid Genetic Algorithm with Bin Packing (HGABP) heuristic has successfully been used to address one dimensional bin packing problem and VM consolidation. The results of the evaluation show that the proposed approach significantly reduces the energy consumption of DC. This research has proposed there are two types of resource allocation schemes namely Commitment Allocation (CA) and Over Commitment Allocation (OCA) in the physical and virtual level resource. These resource allocation schemes help to identify the virtual resource utilization and physical resource availability. Cloud computing always provides IT resources on demand basis, without additional waiting time. The proposed regression based performance model shows the efficiency and performance accuracy of predicting the Hadoop-MapReduce job completion times in the OpenStack private cloud environment using linear regression approach. A cloud task scheduling policy is based on Genetic Algorithm Task Scheduling (GATS) compared with Min-Min and Max-Min task scheduling algorithms. T newline