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SMCoEdge: Simultaneous Multi-server Offloading for Collaborative Mobile Edge Computing

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Algorithms and Architectures for Parallel Processing (ICA3PP 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14491))

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Abstract

Collaborative Mobile Edge Computing (MEC) has emerged as a promising solution for low service delay in computation-intensive Internet of Things (IoT) applications. However, current approaches typically perform offline task partitioning and offload each subtask to an Edge Server (ES) for processing. This leads to varying delays in subtask processing across different ESs, resulting in a high make-span of task offloading. To address this issue, we propose a novel approach called SMCoEdge, which utilizes simultaneous multi-ES offloading to minimize the make-span of task offloading for computation-intensive IoT applications. Specifically, we formulate our problem as a mixed integer non-linear programming problem and prove its NP-hardness. We then decompose our problem into two sub-problems of multi-ES selection and task allocation, and propose a Deep Reinforcement Learning-based Simultaneous Multi-ES Offloading (DRL-SMO) algorithm to effectively solve it. Additionally, we analyze the computation complexity of DRL-SMO. Our extensive simulation results demonstrate that SMCoEdge outperforms state-of-the-art approaches by reducing make-span by 18.93% while maintaining a low offloading failure rate.

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Acknowledgment

This work is supported in part by grants from the National Natural Science Foundation of China (No. 62272117) and the Joint Foundation of Guangzhou and Universities on Basic and Applied Basic Research (202201020126), the National Key R &D Program of China (2022YFE0201400), the Beijing Natural Science Foundation (No. 4232028), the National Natural Science Foundation of China (No. 62172046, 62372047), the Special Project of Guangdong Provincial Department of Education in Key Fields of Colleges and Universities (2021ZDZX1063), the Zhuhai Basic and Applied Basic Research Foundation (2220004002619), the Joint Project of Production, Teaching and Research of Zhuhai (2220004002686, ZH22017001210133PWC, and 2220004002686), the Guangdong Key Lab of AI and Multi-modal Data Processing, BNU-HKBU United International College (UIC), Zhuhai (No. 2020KSYS007), the UIC General project (No. R0200005-22), the UIC Start-up Research Fund (No. R72021202), the Science and Technology Projects of Social Development in Zhuhai (No. 2320004000213), the Guangdong Basic and Applied Basic Research Foundation (No. 2022A1515011583 and No. 2023A1515011562), the One-off Tier 2 Start-up Grant (2020/2021) of Hong Kong Baptist University (Ref. RC-OFSGT2/20-21/COMM/002), the Startup Grant (Tier 1) for New Academics AY2020/21 of Hong Kong Baptist University, National Natural Science Foundation of China (No. 62202402), the Germany/Hong Kong Joint Research Scheme sponsored by the Research Grants Council of Hong Kong and the German Academic Exchange Service of Germany (No. G-HKBU203/22), and the Hong Kong RGC Early Career Scheme (No. 22202423)

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Authors and Affiliations

  1. Hong Kong Baptist University, Hong Kong, China

    Changfu Xu, Yupeng Li & Haodong Zou

  2. BNU-HKBU United International College, Zhuhai, China

    Changfu Xu, Haodong Zou, Weijia Jia & Tian Wang

  3. The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China

    Xiaowen Chu

  4. BNU-UIC Institute of Artificial Intelligence and Future Networks, Beijing Normal University, Zhuhai, China

    Weijia Jia & Tian Wang

Authors
  1. Changfu Xu
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  2. Yupeng Li
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  3. Xiaowen Chu
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  4. Haodong Zou
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  5. Weijia Jia
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  6. Tian Wang
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Correspondence to Tian Wang .

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Editors and Affiliations

  1. Royal Melbourne Institute of Technology, Melbourne, VIC, Australia

    Zahir Tari

  2. Tianjin University, Tianjin, China

    Keqiu Li

  3. University of Arizona, Tucson, AZ, USA

    Hongyi Wu

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Xu, C., Li, Y., Chu, X., Zou, H., Jia, W., Wang, T. (2024). SMCoEdge: Simultaneous Multi-server Offloading for Collaborative Mobile Edge Computing. In: Tari, Z., Li, K., Wu, H. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2023. Lecture Notes in Computer Science, vol 14491. Springer, Singapore. https://doi.org/10.1007/978-981-97-0808-6_5

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  • DOI: https://doi.org/10.1007/978-981-97-0808-6_5

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  • Print ISBN: 978-981-97-0807-9

  • Online ISBN: 978-981-97-0808-6

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