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A Genetic Algorithm Based Efficient Static Load Distribution Strategy for Handling Large-Scale Workloads on Sustainable Computing Systems

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Intelligent Decision Support Systems for Sustainable Computing

Part of the book series: Studies in Computational Intelligence ((SCI,volume 705))

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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Authors and Affiliations

  1. School of Computer Science and Technology, Xidian University, Xi’an, 710071, China

    Xiaoli Wang

  2. Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117576, Singapore

    Bharadwaj Veeravalli

Authors
  1. Xiaoli Wang
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  2. Bharadwaj Veeravalli
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Correspondence to Bharadwaj Veeravalli .

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Editors and Affiliations

  1. VIT University , Vellore, Tamil Nadu, India

    Arun Kumar Sangaiah

  2. Sci N/w for Innova and Research Exc, Machine Intelligence Research Labs Sci N/w for Innova and Research Exc, Auburn, Washington, USA

    Ajith Abraham

  3. Faculté des Sciences et Technologie, Université Paris-Est Créteil Val-de-Marn Faculté des Sciences et Technologie, Créteil Cedex, France

    Patrick Siarry

  4. Faculty of Engig Computer & Math sci, The University of Adelaide Faculty of Engig Computer & Math sci, Adelaide, South Australia, Australia

    Michael Sheng

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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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  • DOI: https://doi.org/10.1007/978-3-319-53153-3_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-53152-6

  • Online ISBN: 978-3-319-53153-3

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