Abstract
Cloud computing is becoming one of the most disruptive forces that are attracting more and more customers towards it for various kinds of services. The increasing demand for cloud computing technology has given rise to network traffic also, hence it is important to balance the workload arising in the network. Load balancing is necessary for the efficient working of cloud services. Load balancing in the cloud can be both static as well as dynamic in nature. In the current scenario, the ability of cloud systems to adapt to changing conditions is necessary, hence dynamic load balancing is emerging as a hot topic. Load balancing can be either done by task scheduling or virtual machine migration. In this paper, a multi-objective particle swarm optimization for task scheduling is proposed. The objectives taken are makespan time, deadline and cost of communication. The experimental result has shown that the proposed method helped in reducing the makespan time, cost of communication and also completing the task within the deadline.
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