Synergizing Genetic Algorithms And Simulated Annealing For Cloud Load Balancing

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.