Hybrid Task Scheduling Algorithm Based on ANT Colony Optimization and Particle Swarm Optimization for Cloud Environment
DOI:
https://doi.org/10.26438/ijcse/v6i2.324328Keywords:
ACO, PSO, VM, SJF, IAAS, PAAS, SAAS, Data Centre, Cloud Computing, DIAbstract
Cloud computing refers to many different types of services and applications being delivered over the internet cloud. Cloud load balancing is the process of distributing workloads across multiple computing resources. Load balancing is an optimization problem and goal of any optimization is to either minimize effort or to maximize benefit. The effort or the benefit can be usually expressed as a function of certain design variables. Hence, optimization is the process of finding the conditions that give the maximum or the minimum value of a function. Load balancing is a problem where you try to minimize value of parameters like Makespan time, Response Time, etc. and increase the utilization of cloud resources. Metaheuristic algorithms are a natural solution to the problem of load balancing in cloud. But these algorithms as such do not provide a complete solution. This paper proposes a hybrid of Particle Swarm optimization and Ant Colony optimization for load balancing of tasks on cloud resources.
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