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An Algorithm Towards Energy-Efficient Scheduling for Real-Time Tasks Under Cloud Computing Environment

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Geo-Spatial Knowledge and Intelligence (GSKI 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 848))

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Abstract

This paper proposes a task scheduling algorithm called AKAM(Adaptive KNN and Adaptive Min-min), which can improve the real-time performance and energy consumption of cloud resource scheduling and allocation. The proposed AKAM based on Min-min task scheduling algorithm and KNN algorithm. Firstly, the Qos requirements contained in the user request task are applied to the KNN algorithm to select the appropriate resources for the task. Secondly, based on Min-min algorithm, we establish task slack and virtual machine threshold to complete task scheduling. Simulation results show the efficiency of AKAM.

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Acknowledgments

This research is supported by the National Key Research and Development Program of China (No. 2015BAF28B01), and Shandong Province Key Research and Development Program (No. 2016GGX103006).

Author information

Authors and Affiliations

  1. College of Information Science and Engineering, No. 238 Songling Road, Laoshan District, Qingdao, Shandong, China

    Tongtong Sun & Ruichun Tang

  2. School of Information and Technology, Qingdao University of Science and Technology, No. 99 Songling Road, Qingdao, China

    Ye Tao

Authors
  1. Tongtong Sun
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  2. Ye Tao
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  3. Ruichun Tang
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Corresponding author

Correspondence to Ruichun Tang .

Editor information

Editors and Affiliations

  1. Beijing Institute of Technology, Beijing, China

    Hanning Yuan

  2. Beijing Institute of Technology, Beijing, China

    Jing Geng

  3. Beijing Institute of Technology, Beijing, China

    Chuanlu Liu

  4. Wuhan University, Wuhan, China

    Fuling Bian

  5. Beijing Institute of Technology, Beijing, China

    Tisinee Surapunt

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Sun, T., Tao, Y., Tang, R. (2018). An Algorithm Towards Energy-Efficient Scheduling for Real-Time Tasks Under Cloud Computing Environment. In: Yuan, H., Geng, J., Liu, C., Bian, F., Surapunt, T. (eds) Geo-Spatial Knowledge and Intelligence. GSKI 2017. Communications in Computer and Information Science, vol 848. Springer, Singapore. https://doi.org/10.1007/978-981-13-0893-2_60

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  • DOI: https://doi.org/10.1007/978-981-13-0893-2_60

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-0892-5

  • Online ISBN: 978-981-13-0893-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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