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An Estimation-Based Task Load Balancing Scheduling in Spot Clouds

Abstract : Cloud computing is a computing paradigm in which users can rent computing resources from service providers according to their requirements. Cloud computing based on the spot market helps a user to obtain resources at a lower cost. However, these resources may be unreliable. In this paper, we propose an estimation-based distributed task workflow scheduling scheme that reduces the estimated generation compared to Genetic Algorithm (GA). Moreover, our scheme executes a user’s job within selected instances and stretches the user’s cost. The simulation results, based on a before-and-after estimation comparison, reveal that the task size is determined based on the performance of each instance and the task is distributed among the different instances. Therefore, our proposed estimation-based task load balancing scheduling technique achieves the task load balancing according to the performance of instances.
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Submitted on : Friday, November 25, 2016 - 2:47:10 PM
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Daeyong Jung, Heeseok Choi, Daewon Lee, Heonchang Yu, Eunyoung Lee. An Estimation-Based Task Load Balancing Scheduling in Spot Clouds. 11th IFIP International Conference on Network and Parallel Computing (NPC), Sep 2014, Ilan, Taiwan. pp.571-574, ⟨10.1007/978-3-662-44917-2_55⟩. ⟨hal-01403147⟩



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