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Load Balance Aware Genetic Algorithm for Task Scheduling in Cloud Computing

  • Conference paper
Simulated Evolution and Learning (SEAL 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8886))

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Abstract

This paper proposes to solve the task scheduling problem in cloud computing by using a load balance aware genetic algorithm (LAGA) with Min-min and Max-min methods. Task scheduling problems are of great importance in cloud computing, and become especially challenging when taking load balance into account. Our proposed LAGA algorithm has several advantages when solving this kind of problems. Firstly, by introducing the time load balance (TLB) model to help establish the fitness function with makespan, the algorithm benefits from the ability to find the solution that performs best on load balance among a set of solutions with the same makespan. More importantly, the interaction between makespan and TLB helps the algorithm to minimize makespan in the same time. Secondly, Min-min and Max-min methods are used to produce promising individuals at the beginning of evolution, leading to noticeable improvement of evolution efficiency. We evaluated LAGA on several task scheduling problems and compared with a Min-min, Max-min improved version of genetic algorithm (MMGA), which does not use the TLB strategy. The results show that LAGA can obtain very competitive results with good load balancing properties, and outperform MMGA in both makespan and TLB objectives.

This work was supported in part by the National High-Technology Research and Development Program (863 Program) of China No.2013AA01A212, in part by the National Natural Science Fundation of China (NSFC) with No. 61402545, 61332002, and 61300044, and in part by the NSFC for Distinguished Young Scholars with No. 61125205.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Sun Yat-sen University, Guangzhou, 510006, China

    Zhi-Hui Zhan

  2. Key Lab. Machine Intelligence and Advanced Computing, Ministry of Education, China

    Ying-Lin

  3. Engineering Research Center of Supercomputing Engineering Software, MOE, China

    Yue-Jiao Gong

  4. Education Department of Guangdong Province, Key Lab. Software Technology, China

    Jun Zhang

  5. School of Sofware Engineering, Sun Yat-sen University, Guangzhou, 510006, China

    Ge-Yi Zhang

  6. Department of Psychology, Sun Yat-sen university, 510275, China

    Ying-Lin

Authors
  1. Zhi-Hui Zhan
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  2. Ge-Yi Zhang
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  3. Ying-Lin
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  4. Yue-Jiao Gong
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  5. Jun Zhang
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Editor information

Editors and Affiliations

  1. Otago University, Dunedin, New Zealand

    Grant Dick

  2. Victoria University of Welling, New Zealand

    Will N. Browne

  3. University of Otago, Dunedin, New Zealand

    Peter Whigham

  4. Unitec Institute of Technology, Victoria University of Wellington, New Zealand

    Mengjie Zhang

  5. Le Quy Don Technical University, Hanoi, Vietnam

    Lam Thu Bui

  6. Department of Computer Science and Intelligent Systems, Graduate School of Engineering, Osaka Prefecture University, 1-1 Gakuen-cho, Naka-ku, 599-8531, Sakai, Osaka, Japan

    Hisao Ishibuchi

  7. Department of Computing, University of Surrey, GU2 7XH, Guildford, Surrey, UK

    Yaochu Jin

  8. RMIT University, Melbourne, Australia

    Xiaodong Li

  9. Department of Electrical & Electronic Engineering, Xi’an Jiaotong-Liverpool University, Suzhou, China

    Yuhui Shi

  10. Indian Institute of Information Technology and Management, Gwalior, India

    Pramod Singh

  11. Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, 117576, Singapore, Singapore

    Kay Chen Tan

  12. USTC-Birmingham Joint Research Institute in Intelligent Computation and Its Applications (UBRI), School of Computer Science and Technology, University of Science and Technology of China, 230027, Hefei, China

    Ke Tang

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© 2014 Springer International Publishing Switzerland

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Zhan, ZH., Zhang, GY., Ying-Lin, Gong, YJ., Zhang, J. (2014). Load Balance Aware Genetic Algorithm for Task Scheduling in Cloud Computing. In: Dick, G., et al. Simulated Evolution and Learning. SEAL 2014. Lecture Notes in Computer Science, vol 8886. Springer, Cham. https://doi.org/10.1007/978-3-319-13563-2_54

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  • DOI: https://doi.org/10.1007/978-3-319-13563-2_54

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13562-5

  • Online ISBN: 978-3-319-13563-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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