ABSO: an energy-efficient multi-objective VM consolidation using adaptive beetle swarm optimization on cloud environment

B. Hariharan, R. Siva, S. Kaliraj, P. N.Senthil Prakash

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)

Abstract

Cloud computing is a powerful way to provide a suitable platform for data centers and to store data. Along with the so many benefits, there are still some management issues that need to be investigated. Although cloud computing seems to be a very attractive implementation it is facing incredible energy consumption and costs concerns. To avoid energy consumption, a VM consolidation and migration approach is introduced. The main objective of VM consolidation is to perform more jobs while consuming less amount of power. To achieve this, in this paper multi-objective energy-efficient VM consolidation using adaptive beetle swarm optimization (ABSO) algorithm is proposed. The proposed ABSO is a hybridization of particle swarm optimization (PSO) and Beetle swarm optimization (BSO).The proposed method presented with efficient solution representation, derivation of efficient fitness function (or multi-objective function) along with PSO and BSO operator. The effectiveness of the approach is analyzed based on the different evaluation measures and effectiveness is compared with different methods. From the results, our proposed approach consumes only 8.234 J energy for scheduling 100 tasks which are 10.616 J for BSO-based VM consolidation, 11.754 J for PSO-based VM consolidation, and 13.545 J for GA-based VM consolidation.

Original languageEnglish
Pages (from-to)2185-2197
Number of pages13
JournalJournal of Ambient Intelligence and Humanized Computing
Volume14
Issue number3
DOIs
Publication statusPublished - 03-2023

All Science Journal Classification (ASJC) codes

  • General Computer Science

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