A Two-Stage Heuristic Hybrid Evolutionary Algorithm for Virtual Machine Migration and Placement in Cloud Bursting
Srividhya V1, S. Balamurali2
1Srividhya V, Department of Computer Applications, Kalasalingam Academy of Research and Education College, Krishnankoil (Tamil Nadu), India.
2Dr. S. Balamurali, Department of Computer Applications, Kalasalingam Academy of Research and Education College, Krishnankoil (Tamil Nadu), India.
Manuscript received on 30 November 2019 | Revised Manuscript received on 19 December 2019 | Manuscript Published on 31 December 2019 | PP: 430-435 | Volume-8 Issue-4S2 December 2019 | Retrieval Number: D10951284S219/2019©BEIESP | DOI: 10.35940/ijrte.D1095.1284S219
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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: The platform for cloud computing offers virtualization and a dynamic pool of resources to the consumers of the cloud. The acceptance and demand of cloud is growing on a regular basis. Cloud computing offers utility-based consumer services across the globe on a pay as you go strategy. Live Virtual Machine (VM) migration sets the basic foundation for cloud management. It hasakey role in reducing operating expenditure and revising quality of service without disruption of cloud services running in the VM. A lot of research has been done to yield better performance in live VM migration and has seen noteworthy development and accomplishment. However, some crucial problems require results and enhancement. With the growth of new cloud computing models like Mobile Edge Computing, certain problems related to optimization need to be addressed. The primary aim of this research work is to emphasize on optimum functioning of live migration. A migration algorithm to consolidate the computational resources, storage resources and network resources dynamically with a two-stage heuristic hybrid evolutionary algorithm is discussed. The resources are consolidated to cut down the energy and cost utilization depending upon the evolutionary Particle Swarm Optimization and the Ant Colony Optimization algorithms. These algorithms can rapidly identify the migrating virtual machines and locate their positions respectively.
Keywords: Cloud bursting, Cloud computing, VM Migration.
Scope of the Article: Innovative Sensing Cloud and Systems