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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Jung, D., Choi, H., Lee, D., Yu, H., Lee, E. (2014). An Estimation-Based Task Load Balancing Scheduling in Spot Clouds. In: Hsu, CH., Shi, X., Salapura, V. (eds) Network and Parallel Computing. NPC 2014. Lecture Notes in Computer Science, vol 8707. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44917-2_55
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DOI: https://doi.org/10.1007/978-3-662-44917-2_55
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-44916-5
Online ISBN: 978-3-662-44917-2
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