Synergizing Genetic Algorithms And Simulated Annealing For Cloud Load Balancing
DOI:
https://doi.org/10.29284/jg6xc115Keywords:
cloud computing, task scheduling, load balancing, genetic algorithm, simulated annealing algorithm.Abstract
The growing demand of cloud computing services using large data centers usually results in imbalances, greater consumption of energy, and escalated operating costs in cloud systems. The issue of load balancing and energy efficiency in clouds is a major problem. Efficient load balancing depends upon efficient resource allocation and scheduling of tasks. There are a number of strategies that can be used to address this problem and reduce the use of energy in cloud systems. Use of collective intelligence algorithms and metaheuristic strategy is one of the vehicles. This paper has shown that the genetic algorithm can be improved by the use of the simulated annealing technique which can have a profound effect in the reduction of the total time needed to provide the applications with the resources. This causes better performance of the system, better load balancing, lesser energy utilization and less operation costs. The results of the simulation show that the proposed strategy is better than the baseline load balancing algorithm of virtual machines by more than 60 percent with better energy efficiency because of the better resource allocation.
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This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
