Load balanced task scheduling for cloud computing: a probabilistic approach

Abstract

Load balancing is the process of distributing customer tasks among multiple computing resources, such as virtual machines (VMs), servers and networks. It is a major concern in cloud computing as the number of customers demanding the service is growing exponentially. An efficient load balancing approach can monitor the load of the VMs proactively and assigns the customer tasks to the VMs accordingly. This paper presents a load balanced task scheduling algorithm in the cloud, which is based on probability theory. The proposed algorithm is shown to be a 2-approximation algorithm with a time complexity of O(lm), where l is the number of customer tasks and m is the number of VMs. The algorithm is simulated extensively. The simulation results demonstrate that our proposed algorithm can remarkably balance the load of the VMs as compared to the existing algorithms in four different performance measures, namely standard deviation of VM loads, maximum load, minimum load and zero load. The performance is also validated through statistical test by means of analysis of variance and 95% confidence interval.

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