Energy Aware Resource Allocation in Cloud Datacenter
Manasa H.B1, Anirban Basu2
1Dr. Anirban Basu, Dept of Computer Science & Engg,- R&D Centre East Point College of Engineering & Technology, Bangalore, India.
2Dr. Anirban Basu, Dept of Computer Science & Engg,- R&D Centre, East Point College of Engineering & Technology, Bangalore, India.
Manuscript received on May 29, 2013. | Revised Manuscript received on June 08, 2013. | Manuscript published on June 30, 2013. | PP: 277-281 | Volume-2, Issue-5, June 2013. | Retrieval Number: E1792062513/2013©BEIESP

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Abstract: The greatest environmental challenge today is global warming, which is caused by carbon emissions. Energy crisis brings green computing, and green computing needs algorithms and mechanisms to be redesigned for energy efficiency. Green IT refers to the study and practice of using computing resources in an efficient, effective and economic way. Currently, a large number of cloud computing systems waste a tremendous amount of energy and emit a considerable amount of carbon dioxide. Thus, it is necessary to significantly reduce pollution and substantially lower energy usage. The proposed energy aware resource allocation provision data center resources to client applications in a way that improves energy efficiency of the data center, while delivering the negotiated Quality of Service (QoS). In particular, in this paper we conduct a survey of research in energy-efficient computing and propose: architectural principles for energy-efficient management of Clouds; energy-efficient resource allocation policies and scheduling algorithms considering QoS expectations and power usage characteristics of the devices. It is validated by conducting a performance evaluation study using Cloud Sim toolkit. Green Cloud Computing aims at a processing infrastructure that combines flexibility, quality of services, and reduced energy utilization. In order to achieve this objective, the management solution must regulate the internal settings to address the pressing issue of data center over-provisioning related to the need to match the peak demand. In this context, propose an integrated solution for resource management based on VMs placement and VMs allocation policies. This work introduces the system management model, analyzes the system’s behavior, describes the operation principles, and presents a use case scenario. To simulate the approach of organization, theory and implementation of migration policies and reallocation changes were made as improvements in the code of Cloud Sim framework.
Keywords: Energy efficiency, Green IT, Cloud computing, migration, resource management, virtualization.