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A Deep Reinforcement Learning-Based Approach to the Scheduling of Multiple Workflows on Non-dedicated Edge Servers

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Parallel and Distributed Computing, Applications and Technologies (PDCAT 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 13148))

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Abstract

Prompted by the remarkable progress in mobile communication technologies, more and more users are starting to execute their workflow applications on the mobile edge computing environment. Scheduling multiple parallel workflows on a non-dedicated edge server is a great challenge because of different users’ requirements. In this paper, we propose an approach based on Deep Reinforcement Learning (DRL) to schedule multiple workflows on an edge server with multiple heterogeneous CPUs to minimise the violation rate of service level agreement of workflows. The effectiveness of our proposed approach is evaluated by simulation experiments based on a set of real-world scientific workflows. The results show that our approach performs better than the current state-of-the-art approaches applied to similar problems.

Supported in part by the National Natural Science Foundation of China under Grant 61662052, in part by the Natural Science Foundation of Inner Mongolia Autonomous Region under Grant 2021MS06002, in part by he Science and Technology Planning Project of Inner Mongolia Autonomous Region under Grant 2021GG0155, and in part by the Major Research Plan of Inner Mongolia Natural Science Foundation under Grant 2019ZD15.

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Author information

Authors and Affiliations

  1. Engineering Research Center of Ecological Big Data, Ministry of Education, Hohhot, 010021, China

    Yongqiang Gao

  2. Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software, Hohhot, 010021, China

    Yongqiang Gao & Ke Feng

  3. College of Computer Science, Inner Mongolia University, Hohhot, 010021, China

    Yongqiang Gao & Ke Feng

Authors
  1. Yongqiang Gao
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  2. Ke Feng
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Corresponding author

Correspondence to Yongqiang Gao .

Editor information

Editors and Affiliations

  1. Sun Yat-sen University, Guangzhou, Guangdong, China

    Hong Shen

  2. Sun Yat-sen University, Guangzhou, China

    Yingpeng Sang

  3. Shenzhen Institute of Advanced Technology, Shenzhen, China

    Yong Zhang

  4. Sun Yat-sen University, Guangzhou, China

    Nong Xiao

  5. University of Georgia, Athens, GA, USA

    Hamid R. Arabnia

  6. University of Utah, Salt Lake City, USA

    Geoffrey Fox

  7. Western Michigan University, Kalamazoo, MI, USA

    Ajay Gupta

  8. Stevens Institute of Technology, Hoboken, NJ, USA

    Manu Malek

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Gao, Y., Feng, K. (2022). A Deep Reinforcement Learning-Based Approach to the Scheduling of Multiple Workflows on Non-dedicated Edge Servers. In: Shen, H., et al. Parallel and Distributed Computing, Applications and Technologies. PDCAT 2021. Lecture Notes in Computer Science(), vol 13148. Springer, Cham. https://doi.org/10.1007/978-3-030-96772-7_24

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  • DOI: https://doi.org/10.1007/978-3-030-96772-7_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-96771-0

  • Online ISBN: 978-3-030-96772-7

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