skip to main content
10.1145/3570361.3614064acmconferencesArticle/Chapter ViewAbstractPublication PagesmobicomConference Proceedingsconference-collections
demonstration

Task Offloading with Multi-cluster Collaboration for Computing and Network Convergence

Published: 02 October 2023 Publication History

Abstract

Edge computing servers have been widely deployed in recent years to address the requirements of diverse tasks that are sensitive to delays and computationally intensive. However, due to their independent nature and uneven distribution of service requests, certain clusters may be relatively idle, while others may be overloaded. This situation can result in increased latency for certain tasks, and it prevents the full utilization of resources in the edge clusters. To mitigate this problem, we design and implement a prototype testbed for task offloading, aimed at achieving computing and network convergence. This testbed facilitates collaboration among multiple edge computing clusters. We construct multiple clusters using Intel NUC mini computers and incorporate key enabling technologies into the system. We assess the testbed's performance by employing multiple video processing services that require low latency and high computational capacity. In scenarios with uneven service requests, load balancing can be achieved across the edge computing clusters, resulting in reduced response latency for user tasks.

References

[1]
Chang Shu, Zhiwei Zhao, Yunpeng Han, Geyong Min, and Hancong Duan. Multi-user offloading for edge computing networks: a dependency-aware and latency-optimal approach. IEEE Internet of Things Journal, 7(3):1678--1689, 2020.
[2]
Yuyi Mao, Changsheng You, Jun Zhang, Kaibin Huang, and Khaled B. Letaief. A survey on mobile edge computing: The communication perspective. IEEE Communications Surveys Tutorials, 19(4):2322--2358, 2017.
[3]
Shuo Wang, Xing Zhang, Yan Zhang, Lin Wang, Juwo Yang, and Wenbo Wang. A survey on mobile edge networks: Convergence of computing, caching and communications. IEEE Access, 5:6757--6779, 2017.
[4]
Yang Li, Xing Zhang, Yukun Sun, Junlin Liu, Bo Lei, and Wenbo Wang. Joint offloading and resource allocation with partial information for multi-user edge computing. In 2022 IEEE Globecom Workshops (GC Wkshps), pages 1736--1741, 2022.
[5]
Yukun Sun, Bo Lei, Junlin Liu, Haonan Huang, Xing Zhang, Jing Peng, and Wenbo Wang. Computing power network: A survey. to appear in China Communications, 2023. arXiv preprint arXiv:2210.06080.
[6]
Jinke Ren, Guanding Yu, Yinghui He, and Geoffrey Ye Li. Collaborative cloud and edge computing for latency minimization. IEEE Transactions on Vehicular Technology, 68(5):5031--5044, 2019.
[7]
Jiagang Liu, Ju Ren, Yongmin Zhang, Xuhong Peng, Yaoxue Zhang, and Yuanyuan Yang. Efficient dependent task offloading for multiple applications in MEC-cloud system. IEEE Transactions on Mobile Computing, 22(4):2147--2162, 2023.
[8]
Rongping Lin, Tianze Xie, Shan Luo, Xiaoning Zhang, Yong Xiao, Bill Moran, and Moshe Zukerman. Energy-efficient computation offloading in collaborative edge computing. IEEE Internet of Things Journal, 9(21):21305--21322, 2022.
[9]
Guang Chen, Yueyun Chen, Zhiyuan Mai, Conghui Hao, Meijie Yang, and Liping Du. Incentive-based distributed resource allocation for task offloading and collaborative computing in MEC-enabled networks. IEEE Internet of Things Journal, 10(10):9077--9091, 2023.

Cited By

View all
  • (2024)Computation Rate Maximization for Wireless Powered Edge Computing With Multi-User CooperationIEEE Open Journal of the Communications Society10.1109/OJCOMS.2024.3358064(1-1)Online publication date: 2024
  • (2024)Joint Task Partitioning and Parallel Scheduling in Device-Assisted Mobile Edge NetworksIEEE Internet of Things Journal10.1109/JIOT.2023.334106211:8(14058-14075)Online publication date: 15-Apr-2024

Index Terms

  1. Task Offloading with Multi-cluster Collaboration for Computing and Network Convergence

      Recommendations

      Comments

      Information & Contributors

      Information

      Published In

      cover image ACM Conferences
      ACM MobiCom '23: Proceedings of the 29th Annual International Conference on Mobile Computing and Networking
      October 2023
      1605 pages
      ISBN:9781450399906
      DOI:10.1145/3570361
      Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).

      Sponsors

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 02 October 2023

      Check for updates

      Qualifiers

      • Demonstration

      Funding Sources

      Conference

      ACM MobiCom '23
      Sponsor:

      Acceptance Rates

      Overall Acceptance Rate 440 of 2,972 submissions, 15%

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • Downloads (Last 12 months)234
      • Downloads (Last 6 weeks)13
      Reflects downloads up to 17 Feb 2025

      Other Metrics

      Citations

      Cited By

      View all
      • (2024)Computation Rate Maximization for Wireless Powered Edge Computing With Multi-User CooperationIEEE Open Journal of the Communications Society10.1109/OJCOMS.2024.3358064(1-1)Online publication date: 2024
      • (2024)Joint Task Partitioning and Parallel Scheduling in Device-Assisted Mobile Edge NetworksIEEE Internet of Things Journal10.1109/JIOT.2023.334106211:8(14058-14075)Online publication date: 15-Apr-2024

      View Options

      Login options

      View options

      PDF

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      Figures

      Tables

      Media

      Share

      Share

      Share this Publication link

      Share on social media