Tri-Objective NSGA-II Based Methods for Load Balancing
Siddhartha Dwivedi1, Divya Kumar2
1Siddhartha Dwivedi, Department of Information Technology, Motilal Nehru National Institute of Technology, Prayagraj (U.P), India.
2Divya Kumar, Assistant Professor, Department of Computer Science and Engineering, Motilal Nehru National Institute of Technology, Prayagraj (U.P), India.
Manuscript received on 04 December 2019 | Revised Manuscript received on 12 December 2019 | Manuscript Published on 31 December 2019 | PP: 283-289 | Volume-9 Issue-2S December 2019 | Retrieval Number: B10661292S19/2019©BEIESP | DOI: 10.35940/ijitee.B1066.1292S19
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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 rapid rise of virtual machines are affecting the daily lives of people profusely. It is clear that to cater to such huge amounts of requests, servers which can withstand the upper bound of those requests must be maintained. In this paper, we propose a model based on Evolutionary Algorithms which attempts to schedule given tasks to virtual machines in such a manner, so as to minimise the load imbalance among the different machines available. We show that using a greedy approach with certain optimisation functions, a workable solution can be reached which would help reduce this “upper bound” mentioned above. Through it, one can expect the load on any particular machine to not exceed a certain amount and be distributed amongst all virtual machines.
Keywords: Genetic Algorithms, Load Balancing, Makespan, NSGA-II, Virtual Machines.
Scope of the Article: Encryption Methods and Tools