IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v15y2023i11p357-d1270552.html
   My bibliography  Save this article

Task Scheduling for Federated Learning in Edge Cloud Computing Environments by Using Adaptive-Greedy Dingo Optimization Algorithm and Binary Salp Swarm Algorithm

Author

Listed:
  • Weihong Cai

    (Department of Computer, Shantou University, Shantou 515063, China)

  • Fengxi Duan

    (Department of Computer, Shantou University, Shantou 515063, China)

Abstract

With the development of computationally intensive applications, the demand for edge cloud computing systems has increased, creating significant challenges for edge cloud computing networks. In this paper, we consider a simple three-tier computational model for multiuser mobile edge computing (MEC) and introduce two major problems of task scheduling for federated learning in MEC environments: (1) the transmission power allocation (PA) problem, and (2) the dual decision-making problems of joint request offloading and computational resource scheduling (JRORS). At the same time, we factor in server pricing and task completion, in order to improve the user-friendliness and fairness in scheduling decisions. The solving of these problems simultaneously ensures both scheduling efficiency and system quality of service (QoS), to achieve a balance between efficiency and user satisfaction. Then, we propose an adaptive greedy dingo optimization algorithm (AGDOA) based on greedy policies and parameter adaptation to solve the PA problem and construct a binary salp swarm algorithm (BSSA) that introduces binary coding to solve the discrete JRORS problem. Finally, simulations were conducted to verify the better performance compared to the traditional algorithms. The proposed algorithm improved the convergence speed of the algorithm in terms of scheduling efficiency, improved the system response rate, and found solutions with a lower energy consumption. In addition, the search results had a higher fairness and system welfare in terms of system quality of service.

Suggested Citation

  • Weihong Cai & Fengxi Duan, 2023. "Task Scheduling for Federated Learning in Edge Cloud Computing Environments by Using Adaptive-Greedy Dingo Optimization Algorithm and Binary Salp Swarm Algorithm," Future Internet, MDPI, vol. 15(11), pages 1-23, October.
    • Handle: RePEc:gam:jftint:v:15:y:2023:i:11:p:357-:d:1270552
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/15/11/357/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1999-5903/15/11/357/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hernán Peraza-Vázquez & Adrián F. Peña-Delgado & Gustavo Echavarría-Castillo & Ana Beatriz Morales-Cepeda & Jonás Velasco-Álvarez & Fernando Ruiz-Perez, 2021. "A Bio-Inspired Method for Engineering Design Optimization Inspired by Dingoes Hunting Strategies," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-19, September.
    2. Mahmoud H. Alrefaei & Sigrún Andradóttir, 1999. "A Simulated Annealing Algorithm with Constant Temperature for Discrete Stochastic Optimization," Management Science, INFORMS, vol. 45(5), pages 748-764, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Shamsuddin Ahmed, 2013. "Performance of derivative free search ANN training algorithm with time series and classification problems," Computational Statistics, Springer, vol. 28(5), pages 1881-1914, October.
    2. Wang, Honggang, 2012. "Retrospective optimization of mixed-integer stochastic systems using dynamic simplex linear interpolation," European Journal of Operational Research, Elsevier, vol. 217(1), pages 141-148.
    3. Sigrún Andradóttir & Andrei A. Prudius, 2009. "Balanced Explorative and Exploitative Search with Estimation for Simulation Optimization," INFORMS Journal on Computing, INFORMS, vol. 21(2), pages 193-208, May.
    4. Zhang, Jingrui & Li, Zhuoyun & Wang, Beibei, 2021. "Within-day rolling optimal scheduling problem for active distribution networks by multi-objective evolutionary algorithm based on decomposition integrating with thought of simulated annealing," Energy, Elsevier, vol. 223(C).
    5. Katsumi Morikawa & Katsuhiko Takahashi & Daisuke Hirotani, 2018. "Performance evaluation of candidate appointment schedules using clearing functions," Journal of Intelligent Manufacturing, Springer, vol. 29(3), pages 509-518, March.
    6. L. Jeff Hong & Barry L. Nelson, 2006. "Discrete Optimization via Simulation Using COMPASS," Operations Research, INFORMS, vol. 54(1), pages 115-129, February.
    7. Gülcü, Ayla & Akkan, Can, 2020. "Robust university course timetabling problem subject to single and multiple disruptions," European Journal of Operational Research, Elsevier, vol. 283(2), pages 630-646.
    8. Wang, Kang & Wang, Jianzhou & Zeng, Bo & Lu, Haiyan, 2022. "An integrated power load point-interval forecasting system based on information entropy and multi-objective optimization," Applied Energy, Elsevier, vol. 314(C).
    9. Alrefaei, Mahmoud H. & Alawneh, Ameen J., 2005. "Solution quality of random search methods for discrete stochastic optimization," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 68(2), pages 115-125.
    10. Jie Xu & Barry L. Nelson & L. Jeff Hong, 2013. "An Adaptive Hyperbox Algorithm for High-Dimensional Discrete Optimization via Simulation Problems," INFORMS Journal on Computing, INFORMS, vol. 25(1), pages 133-146, February.
    11. Lysa Porth & Milton Boyd & Jeffrey Pai, 2016. "Reducing Risk Through Pooling and Selective Reinsurance Using Simulated Annealing: An Example from Crop Insurance," The Geneva Papers on Risk and Insurance Theory, Springer;International Association for the Study of Insurance Economics (The Geneva Association), vol. 41(2), pages 163-191, September.
    12. Ziyan Zhao & Pengkai Xiao & Jiacun Wang & Shixin Liu & Xiwang Guo & Shujin Qin & Ying Tang, 2023. "Improved Brain-Storm Optimizer for Disassembly Line Balancing Problems Considering Hazardous Components and Task Switching Time," Mathematics, MDPI, vol. 12(1), pages 1-19, December.
    13. João Claro & Jorge Sousa, 2010. "A multiobjective metaheuristic for a mean-risk static stochastic knapsack problem," Computational Optimization and Applications, Springer, vol. 46(3), pages 427-450, July.
    14. Liu, Xiaomei & Li, Sihan & Gao, Meina, 2024. "A discrete time-varying grey Fourier model with fractional order terms for electricity consumption forecast," Energy, Elsevier, vol. 296(C).
    15. Hemmelmayr, Vera & Doerner, Karl F. & Hartl, Richard F. & Savelsbergh, Martin W.P., 2010. "Vendor managed inventory for environments with stochastic product usage," European Journal of Operational Research, Elsevier, vol. 202(3), pages 686-695, May.
    16. Andrei A. Prudius & Sigrún Andradóttir, 2012. "Averaging frameworks for simulation optimization with applications to simulated annealing," Naval Research Logistics (NRL), John Wiley & Sons, vol. 59(6), pages 411-429, September.
    17. Alfredo Garcia & Stephen D. Patek & Kaushik Sinha, 2007. "A Decentralized Approach to Discrete Optimization via Simulation: Application to Network Flow," Operations Research, INFORMS, vol. 55(4), pages 717-732, August.
    18. Mahmoud H. Alrefaei & Sigrún Andradóttir, 2005. "Discrete stochastic optimization using variants of the stochastic ruler method," Naval Research Logistics (NRL), John Wiley & Sons, vol. 52(4), pages 344-360, June.
    19. Deniz Preil & Michael Krapp, 2023. "Genetic multi-armed bandits: a reinforcement learning approach for discrete optimization via simulation," Papers 2302.07695, arXiv.org.
    20. Qi Zhang & Jiaqiao Hu, 2019. "Simulation Optimization Using Multi-Time-Scale Adaptive Random Search," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 36(06), pages 1-34, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jftint:v:15:y:2023:i:11:p:357-:d:1270552. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.