Chinese Journal on Internet of Things ›› 2022, Vol. 6 ›› Issue (4): 41-52.doi: 10.11959/j.issn.2096-3750.2022.00303

• Theory and Technology • Previous Articles     Next Articles

Collaborative task offloading and resource allocation optimization for intelligent edge devices

Xian LI1, Suzhi BI1,2, Hongru ZENG1, Bin LIN1, Xiaohui LIN1   

  1. 1 College of Electronics and Information Engineering, Shenzhen University, Shenzhen 518060, China
    2 Pengcheng Laboratory, Broadband Communication Research Department, Shenzhen 518066, China
  • Revised:2022-09-23 Online:2022-12-30 Published:2022-12-01
  • Supported by:
    The National Key Research and Development Program(2019YFB1803305);The National Natural Science Foundation of China(61871271);The National Natural Science Foundation of China(62271325);The Major Key Project of PCL Department of Broadband Communication;The Key Project of Department of Education of Guangdong Province(2020ZDZX3050);Guangdong Basic and Applied Basic Research Foundation(2022A1515011219);Guangdong Basic and Applied Basic Research Foundation(2022A1515010973);The Shenzhen Science and Technology Program(20220810142637001);The Shenzhen Science and Technology Program(JCYJ20210324093011030);The Shenzhen Science and Technology Program(JCYJ20190808120415286);The Open Research Project Programme of the State Key Laboratory of Internet of Things for Smart City (University of Macau)(SKL-IoTSC(UM)-2021-2023/ORPF/A03/2022)


In order to deal with the increasingly scarce computing resources, a cooperative edge computing scheme was proposed, which makes full use of the idle resources among users to improve the overall data processing performance.To maximize the user utility, the target problem was formulated as an MINLP (mixed integer non-linear programming), and a learning-optimization-integrated method was proposed to jointly optimize the resource allocation and user offloading decisions.Simulation results show that the proposed scheme can produce a near-optimal solution in sub-second and effectively improve the system utility at least 85.4% compared to the considered benchmark methods.

Key words: mobile edge computing, utility maximization, convex optimization, reinforcement learning

CLC Number: 

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