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A Genetic-Ant-Colony Hybrid Algorithm for Task Scheduling in Cloud System

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Smart Computing and Communication (SmartCom 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10135))

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Abstract

As the task load of cloud system grows bigger, it becomes very important to design an efficiency task scheduling algorithm. This paper proposes a task scheduling algorithm based on genetic algorithm and ant colony optimization algorithm. The hybrid task scheduling algorithm can help the cloud system to complete users’ tasks faster. Simulation experiment results in CloudSim show that, comparing with genetic algorithm and ant colony optimization algorithm alone, the hybrid algorithm has better performance in the aspects of load balancing and optimal time span.

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Acknowledgement

This work is supported by the National Natural Science Foundation of China under Grants NSFC 61672358.

Author information

Authors and Affiliations

  1. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China

    Zhilong Wu, Shubin Cai, Zhijiao Xiao & Zhong Ming

  2. Engineering Research Center of Mobile Internet Application Middleware, Shenzhen, Guangdong Province, China

    Zhilong Wu, Shubin Cai, Zhijiao Xiao & Zhong Ming

  3. College of Mathematics and Information Science, HeBei University, Baoding, Hebei Province, China

    Sheng Xing

Authors
  1. Zhilong Wu
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  2. Sheng Xing
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  3. Shubin Cai
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  4. Zhijiao Xiao
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  5. Zhong Ming
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Corresponding author

Correspondence to Zhong Ming .

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Editors and Affiliations

  1. Pace University, New York, New York, USA

    Meikang Qiu

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Wu, Z., Xing, S., Cai, S., Xiao, Z., Ming, Z. (2017). A Genetic-Ant-Colony Hybrid Algorithm for Task Scheduling in Cloud System. In: Qiu, M. (eds) Smart Computing and Communication. SmartCom 2016. Lecture Notes in Computer Science(), vol 10135. Springer, Cham. https://doi.org/10.1007/978-3-319-52015-5_19

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  • DOI: https://doi.org/10.1007/978-3-319-52015-5_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-52014-8

  • Online ISBN: 978-3-319-52015-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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