Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (1): 72-80.doi: 10.11896/jsjkx.200800088

Special Issue: Intelligent Edge Computing

• Intelligent Edge Computing • Previous Articles     Next Articles

Multi-edge Collaborative Computing Unloading Scheme Based on Genetic Algorithm

GAO Ji-xu, WANG Jun   

  1. School of Communication and Information Engineering,Nanjing University of Posts and Telecommunications,Nanjing 210000,China
  • Received:2020-08-16 Revised:2020-10-18 Online:2021-01-15 Published:2021-01-15
  • About author:GAO Ji-xu,born in 1996,postgraduate.His main research interests include mobile edge computing and IoT.
    WANG Jun,born in 1975,Ph.D,asso-ciate professor.Her main research inte-rests include network architecture of IoT and wireless sensor networks.

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Abstract: As a supplement to cloud computing,edge computing can ensure that the calculation delay meets system requirements when processing computing tasks generated by lOT equipment.Aiming at the problem of insufficient utilization of the remote edge cloud due to the empty window period of the computing task in the traditional offloading scenario,a genetic algorithm-based multi-edge and cloud collaborative computing offloading model (Genetic Algorithm-based Multi-edge Collaborative Computing Offloading Model,GAMCCOM) is proposed.This computing offloading solution combines local edge and remote edge to perform task offloading and uses a genetic algorithm to get the minimum system cost under consideration of both delay and energy consumption at the same time.The results of simulation experiments show that when considering the time delay consumption and energy consumption of the unloading system,the overall cost of this scheme is reduced by 23% compared with the basic three-layer unloading scheme.In the case of considering time delay consumption and energy consumption respectively,the system cost can still be reduced by 17% and 15% respectively.Therefore,the GAMCCOM offloading method can effectively reduce the system cost for different offloading targets of edge computing.

Key words: Collaborative unloading, Computing unloading, Edge computing, Genetic algorithm, Internet of things

CLC Number: 

  • TP393
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