The Journal of China Universities of Posts and Telecommunications ›› 2023, Vol. 30 ›› Issue (3): 78-87.doi: 10.19682/j.cnki.1005-8885.2022.1010

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Prediction based dynamic resource allocation method for edge computing first networking

Zhang Luying, Liu Xiaokai, Li Zhao, Xu Fangmin, Zhao Chenglin   

  1. 1. School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China 2. Beijing Wulian Zhitong Technology Corporation, Beijing 100041, China 3. State Grid Liaoning Electric Power Supply Co. , Ltd. , Shenyang 110004, China
  • Received:2021-09-16 Revised:2023-04-07 Online:2023-06-30 Published:2023-06-30
  • Contact: Xu Fangmin
  • Supported by:
    This work was supported by the National Natural Science Foundation of China ( 61971050 ) , and the Beijing Natural Science Foundation—Haidian Frontier Project ( L202017) .

Abstract: Aiming at the factory with high-complex and multi-terminal in the industrial Internet of things ( IIoT), a hierarchical edge networking collaboration ( HENC ) framework based on the cloud-edge collaboration and computing first networking (CFN) is proposed to improve the capability of task processing with fixed computing resources on the edge effectively. To optimize the delay and energy consumption in HENC, a multi-objective optimization (MOO) problem is formulated. Furthermore, to improve the efficiency and reliability of the system, a resource prediction model based on ridge regression (RR) is proposed to forecast the task size of the next time slot, and an emergency-aware ( EA) computing resource allocation algorithm is proposed to reallocate tasks in edge CFN. Based on the simulation result, the EA algorithm is superior to the greedy resource allocation in time delay, energy consumption, quality of service (QoS) especially with limited computing resources.

Key words: cloud-edge collaboration, computing first networking (CFN), computing resource allocation, multi-objective optimization (MOO)

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