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Multi-Objective Energy-Efficient Virtual Machine Consolidation Using Dynamic Double Threshold-Enhanced Search and Rescue-Based Optimization

Multi-Objective Energy-Efficient Virtual Machine Consolidation Using Dynamic Double Threshold-Enhanced Search and Rescue-Based Optimization

Sweta Singh, Rakesh Kumar, Udai Pratap Rao
Copyright: © 2022 |Volume: 14 |Issue: 1 |Pages: 26
ISSN: 1942-9045|EISSN: 1942-9037|EISBN13: 9781683181019|DOI: 10.4018/IJSSCI.315006
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MLA

Singh, Sweta, et al. "Multi-Objective Energy-Efficient Virtual Machine Consolidation Using Dynamic Double Threshold-Enhanced Search and Rescue-Based Optimization." IJSSCI vol.14, no.1 2022: pp.1-26. http://doi.org/10.4018/IJSSCI.315006

APA

Singh, S., Kumar, R., & Rao, U. P. (2022). Multi-Objective Energy-Efficient Virtual Machine Consolidation Using Dynamic Double Threshold-Enhanced Search and Rescue-Based Optimization. International Journal of Software Science and Computational Intelligence (IJSSCI), 14(1), 1-26. http://doi.org/10.4018/IJSSCI.315006

Chicago

Singh, Sweta, Rakesh Kumar, and Udai Pratap Rao. "Multi-Objective Energy-Efficient Virtual Machine Consolidation Using Dynamic Double Threshold-Enhanced Search and Rescue-Based Optimization," International Journal of Software Science and Computational Intelligence (IJSSCI) 14, no.1: 1-26. http://doi.org/10.4018/IJSSCI.315006

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Abstract

The popularization of the cloud and its need to solve complex engineering application have alarmed energy and environmental concerns among the researchers. Achieving energy efficiency has become one of the most essential aims of the data center, offering more services with minimal energy consumption (EC). VM consolidation aims at adjusting the VMs to fewer PMs by live migration of VMs and then switching off the inactive servers, achieving energy efficiency. However, uncontrolled consolidation could violate the SLA. The paper contributes by considering the optimization problem targeting the EC and the number of VM migrations. Dynamic double threshold with enhanced search and rescue (DDT-ESAR) optimization has been introduced utilizing two thresholds; the first value defines the upper and lower bound for host classification, whereas the other is used to make migration decision. For migration, ESAR has been adopted for the most appropriate PM- VM mapping. The experimental analysis proves the efficiency where EC is computed to be 0.384kWh, SLA violations to be 6.33% and 64 number of migrations.

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