Abstract
A key challenge faced by large-scale computing platforms to go green is the effective utilization of energy at the various processing nodes. Most existing scheduling models assume that processors are able to stay online forever. In reality, processors, however, may have arbitrary unavailable time periods. Hence, if we inadvertently assign tasks to processors without considering the availability constraints, some processors would not be able to finish their assigned workloads. Thus all the unfinished workloads need to be reassigned to other available processors resulting in an inefficient time and energy schedule. In this chapter, we propose a novel processor availability-aware divisible-load scheduling model. Using this model, we design a time-efficient genetic algorithm based global optimization technique to derive an optimal load distribution strategy. Our experimental results show that the proposed algorithm adapts to minimize the processing time, hence the energy consumption too, by over 60% compared to other strategies.
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Acknowledgements
This is a collaborative research work conducted jointly between Department of Electrical and Computer Engineering, National University of Singapore, Singapore, and School of Computer Science and Technology, Xidian University, China and supported by National Natural Science Foundation of China (No. 61402350, No. 6 1472297, and No. 61572391), the Fundamental Research Funds for the Central Universities (No. JB150307) and China Scholarship Council.
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Wang, X., Veeravalli, B. (2017). A Genetic Algorithm Based Efficient Static Load Distribution Strategy for Handling Large-Scale Workloads on Sustainable Computing Systems. In: Sangaiah, A., Abraham, A., Siarry, P., Sheng, M. (eds) Intelligent Decision Support Systems for Sustainable Computing. Studies in Computational Intelligence, vol 705. Springer, Cham. https://doi.org/10.1007/978-3-319-53153-3_2
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