Energy Efficient Resource Allocation using Improved BSP Technique in Cloud Environment
M.J. Abinash1, V. Vasudevan2
1M.J. Abinash, Research Scholar, Department of Information Technology, Kalasalingam Academy of Research and Education Deemed to be University, Krishankoil (Tamil Nadu), India.
2V. Vasudevan, Senior Professor, Department of Information Technology, Kalasalingam Academy of Research and Education Deemed to be University, Krishankoil (Tamil Nadu), India.
Manuscript received on 08 September 2019 | Revised Manuscript received on 17 September 2019 | Manuscript Published on 26 October 2019 | PP: 509-511 | Volume-8 Issue-11S2 September 2019 | Retrieval Number: K108709811S219/2019©BEIESP | DOI: 10.35940/ijitee.K1087.09811S219
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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 allows the user to access their resources from data center. The energy consumed by each resources will leads to low performance in the data center. The existing work deals with heuristic resource allocation which concentrates only on bottleneck resources. This research work proposes BSP algorithm to allocate the data center resources efficiently in terms of dynamic information and energy consumption. BSP is a placement technique which provides high quality placement for resources based on optimization process. The agent provide authentication to each user to ensure the privacy of the system. The overall resource allocation is achieved by BPS technique by performing continuous deployment and ongoing optimization. Continuously deployment is efficiently allocating the resources based on demand risk score. If any virtual machine in the cloud environment is overloaded, the ongoing optimization technique is applied to migrate the resources from overloaded VM to idle VM. The benefit of our improved BSP technique with the comparison of the results of the existing system is shown in performance analysis.
Keywords: Cloud Data Center, Backward Speculative Placement (BSP), Resource Allocation, Resource Prediction.
Scope of the Article: Innovative Sensing Cloud and Systems