Modified Salp Swarm Algorithm based Energy Efficient Resource Allocation in Cloud-Computing Data Centers
Rajesh P. Patel1, Harshad B. Bhadka2

1Rajesh Patel, Research Scholar, C. U. Shah University, Wadhwan, Gujarat.
2Dr. Harshad Bhadka , Dean and Associate Professor, Faculty of Computer Science, C. U. Shah University, Wadhwan, Gujarat.

Manuscript received on September 17, 2019. | Revised Manuscript received on 24 September, 2019. | Manuscript published on October 10, 2019. | PP: 3713-3720 | Volume-8 Issue-12, October 2019. | Retrieval Number: L26531081219/2019©BEIESP | DOI: 10.35940/ijitee.L2653.1081219
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Abstract: In a cloud data centre the consolidation of the virtual machines (VMs) assist to optimize the resources need and diminish the energy consumption. In the consolidation of the VMs the VM placement acts an important role. By considering optimized energy consumption the researchers have developed various algorithms for VM placement. However, these algorithms be deficient in the exploitation mechanism use resourcefully. This paper attend to VM placement issues by offering metaheuristic algorithms that is, the Modified Salp Swarm Algorithm (MSSA) presenting the comparative analysis relating to energy optimization. The comparison are made adjacent to the existing particle swarm optimization (PSO), and salp swarm algorithm (SSA) and the energy consumption results of all the contributing algorithms confirm that the proposed MSSA is more efficient than the other algorithms. The simulation result demonstrates that MSSA outperforms effectively than other presented approaches in optimal VM placement in cloud computing environment with maximal resource use, minimal energy consumption, minimum SLA violation and reduced migration cost.
Keywords: Energy Efficient, Virtual Machine Placement, Migration, Dynamic Resource Allocation, Cloud Computing, Data Centers
Scope of the Article: Cloud Computing