Abstract
It is possible for IT service providers to provide computing resources in an pay-per-use way in Cloud Computing environments. At the same time, terminal users can also get satisfying services conveniently. But if we take only execution time into consideration when scheduling the cloud resources, it may occur serious load imbalance problem between Virtual Machines (VMs) in Cloud Computing environments. In addition to solve this problem, a new task scheduling model is proposed in this paper. In the model, we optimize the task execution time in view of both the task running time and the system resource utilization. Based on the model, a Particle Swarm Optimization (PSO) – based algorithm is proposed. In our algorithm, we improved the standard PSO, and introduce a simple mutation mechanism and a self-adapting inertia weight method by classifying the fitness values. In the end of this paper, the global search performance and convergence rate of our adaptive algorithm are validated by the results of the comparative experiments.
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Liu, Z., Wang, X. (2012). A PSO-Based Algorithm for Load Balancing in Virtual Machines of Cloud Computing Environment. In: Tan, Y., Shi, Y., Ji, Z. (eds) Advances in Swarm Intelligence. ICSI 2012. Lecture Notes in Computer Science, vol 7331. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30976-2_17
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DOI: https://doi.org/10.1007/978-3-642-30976-2_17
Publisher Name: Springer, Berlin, Heidelberg
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