Resource Allocation in Cloud Computing for Energy Efficiency
Deepa R1, Dheeba J2
1Deepa R, B.sc, (Information System Management) 2011, M.sc., (Computer Science) 2014 and studying M.Tech (By Research) in computer Science and Engineering at Vellore Institute of Technology, Vellore, Tamil Nadu, India.
2Dheeba J, Computer science and Engineering from Anna University, Chennai. Associate Professor in School of Computer Science and Engineering at Vellore Institute of Technology, Vellore. Her research interests include medical image analysis, Machine learning approaches and big data analytics.
Manuscript received on July 20, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 1170-1174 | Volume-8 Issue-6, August 2019. | Retrieval Number: F8356088619/2019©BEIESP | DOI: 10.35940/ijeat.F8356.088619
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Cloud computing is a paradigm in which we have virtualized computer systems that deliver services, processing, storage, network, and other fundamental computing resources. Cloud computing enables low cost, device location independence, high reliability, scalability and sustainability. This paper describes the present state of cloud computing research by examining literature, identifying current study trends. We have analyzed the resource allocation method and concluded. It typically designs for high performance that supports the peak resource requirements. After several analyses the power consumption of data center and cloud systems as increased almost several times. There is a lack of research that addresses challenges of managing multiple resources with objective of allocating enough resources for each work load to optimizing power consumption. These papers survey various types of resource allocation algorithms that improve the cloud Infrastructure.
Keywords: Cloud computing, Resource Allocation, power consumption, data center, multiple resources, cloud infrastructure.