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Power Consumption Constrained Task Scheduling Using Enhanced Genetic Algorithms

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Evolutionary Based Solutions for Green Computing

Part of the book series: Studies in Computational Intelligence ((SCI,volume 432))

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Abstract

Two typical challenges in the energy aware tasking scheduling are (1) minimizing energy consumption with execution time constraint and (2) minimizing task execution time with energy consumption constraint. This chapter focuses on the later challenge. It is very important to efficiently schedule tasks in mobile devices and sensor networks that have limited power. The goal is to cooperatively complete a set of tasks among diverse computing resources with given energy. Traditional scheduling algorithms, such as list scheduling, are not very efficient for this scheduling problem. Linear programming optimization method does not fit well since it is a non-linear problem. An enhanced genetic algorithm can solve this problem effectively.

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Author information

Authors and Affiliations

  1. Computer Science Department, Georgia State University, P.O. Box 3994, Atlanta, GA, 30302-3994, USA

    Gang Shen & Yanqing Zhang

Authors
  1. Gang Shen
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  2. Yanqing Zhang
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Corresponding author

Correspondence to Gang Shen .

Editor information

Editors and Affiliations

  1. , Department of Electrical and Computer, North Dakota State University, Fargo, 58108-6050, North Dakota, USA

    Samee Ullah Khan

  2. Institute of Computer Science, Cracow University of Technology, ul. Warszawska 24, Cracow, 31-155, Poland

    Joanna Kołodziej

  3. , Department of Computer Science, North Dakota State University, Fargo, 58108-6050, North Dakota, USA

    Juan Li

  4. School of Information Technologies, University of Sydney, Sydney, 2006, New South Wales, Australia

    Albert Y. Zomaya

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Shen, G., Zhang, Y. (2013). Power Consumption Constrained Task Scheduling Using Enhanced Genetic Algorithms. In: Khan, S., Kołodziej, J., Li, J., Zomaya, A. (eds) Evolutionary Based Solutions for Green Computing. Studies in Computational Intelligence, vol 432. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30659-4_5

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  • DOI: https://doi.org/10.1007/978-3-642-30659-4_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-30658-7

  • Online ISBN: 978-3-642-30659-4

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