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Adaptive Genetic Algorithm with Optimized Operators for Scheduling in Computer Systems

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Intelligent Information Processing XII (IIP 2024)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 703))

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Abstract

Modern computing and networking environments provide the important problems of efficient using such resources as energy and cores or processors. It is based on the possibility of dynamically varying the speed of processors and using parallel calculations in the execution of operations. We consider the NP-hard speed scaling scheduling problem with energy constraints and parallelizable jobs. Each job must be executed on the given number of processors. Processors can vary their speeds dynamically. It is required to assign speeds to jobs and schedule them such that the total completion time is minimized under the given energy budget. An adaptive genetic algorithm with optimized crossover operators is proposed. The optimal recombination problem is solved in the crossover operator. This problem is aimed at searching for the best possible offspring following the well-known gene transmitting property. The experimental evaluation shows that the algorithm outperforms the known metaheuristics and demonstrates the perspectives of using adaptive techniques and optimized operators.

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Acknowledgements

The research was supported by Russian Science Foundation grant N 22-71-10015.

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Authors and Affiliations

  1. Sobolev Institute of Mathematics SB RAS, Omsk, Russia

    Yu. V. Zakharova & M. Yu. Sakhno

Authors
  1. Yu. V. Zakharova
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  2. M. Yu. Sakhno
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Corresponding author

Correspondence to M. Yu. Sakhno .

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Editors and Affiliations

  1. Chinese Academy of Sciences, Beijing, China

    Zhongzhi Shi

  2. University of Oslo, Oslo, Norway

    Jim Torresen

  3. De Montfort University, Leicester, UK

    Shengxiang Yang

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Zakharova, Y.V., Sakhno, M.Y. (2024). Adaptive Genetic Algorithm with Optimized Operators for Scheduling in Computer Systems. In: Shi, Z., Torresen, J., Yang, S. (eds) Intelligent Information Processing XII. IIP 2024. IFIP Advances in Information and Communication Technology, vol 703. Springer, Cham. https://doi.org/10.1007/978-3-031-57808-3_23

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  • DOI: https://doi.org/10.1007/978-3-031-57808-3_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-57807-6

  • Online ISBN: 978-3-031-57808-3

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