A Self-Optimization Based Virtual Machine Scheduling to Workloads in Cloud Computing Environment
Bhupesh Kumar Dewangan1, Amit Agarwal2, Venkatadri M3, Ashutosh Pasricha4
1Bhupesh Kumar Dewangan, Department of Informatics, University of Petroleum and Energy Studies, Dehradun (Uttarakhand), India.
2Amit Agarwal, Department of Cloud Computing and Virtualization, University of Petroleum and Energy Studies, Dehradun (Uttarakhand), India.
3Venkatadri M, Department of CSE, Amity University, (Madhya Pradesh), India.
4Ashutosh Pasricha, Head Account, Schlumberger Oil Field, (New Delhi), India.
Manuscript received on 25 March 2019 | Revised Manuscript received on 06 April 2019 | Manuscript Published on 11 April 2019 | PP: 91-96 | Volume-8 Issue-4C, April 2019 | Retrieval Number: D24270484C19/19©BEIESP
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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: Energy consumption in the cloud computing plays a vital role in operating cost to the service provider and cloud user. The cloud is scalable and it can provide the access as per demand, due to this resource access requests are increasing, and submitted to the server. To manage all request, scheduling is the solution to assign request with the quality of service. To avoid high operating cost, resource scheduling needs to be energy-aware. In this paper, energy-aware resource scheduling in the cloud is proposing. Total resource utilization of each resource has been calculating and energy is optimizing through antlion optimization algorithm to avoid high consumption of power. The resource is identify with its best utilization value and assign to submit workloads as per priority basis. The experimental results of the proposed work are analyzing with existing autonomic frameworks, and it is observed that proposed work is performing utmost.
Keywords: Energy, Resource Cost, Execution Time, Performance, SLA Violation Rate.
Scope of the Article: Cloud Computing