Computer Science ›› 2021, Vol. 48 ›› Issue (3): 281-288.doi: 10.11896/jsjkx.200700025

• Computer Network • Previous Articles     Next Articles

Resource Allocation and Offloading Decision of Edge Computing for Reducing Core Network Congestion

LI Zhen-jiang, ZHANG Xing-lin   

  1. School of Computer Science & Engineering,South China University of Technology,Guangzhou 510006,China
  • Received:2020-07-03 Revised:2020-08-26 Online:2021-03-15 Published:2021-03-05
  • About author:LI Zhen-jiang,born in 1997,postgra-duate.His main research interests include mobile edge computing and so on.
    ZHANG Xing-lin,born in 1987,Ph.D,associate professor,is a member of China Computer Federation.His main research interests include mobile edge computing and mobile crowdsensing.
  • Supported by:
    National Natural Science Foundation of China(61872149),Natural Science Foundation of Guangdong Province for Distinguished Young Scholar (2018B030306010),Guangdong Special Support Program (2017TQ04X482),Pearl River S&T Nova Program of Guangzhou (201806010088) and Fundamental Research Funds for the Central Universities.

Abstract: With the development of mobile Internet and IoT,more and more intelligent end devices are put into use,and a large number of computation-intensive and time-sensitive applications are widely used,such as AR/VR,smart home,and Internet of vehicles.Thus,the traffic in the network is surging,which gradually increases the pressure of the core network,and it is more and more difficult to control the network delay.At this time,the cloud-edge collaborative computing paradigm is proposed as a solution.To solve the problem of core network traffic control between the cloud and edges,this paper proposes a resource allocation and offloading decision algorithm to reduce the traffic of cloud-edge communication.First,this paper uses the designed resource allocation algorithm based on the divided time slot to improve the processed traffic of edges.Then,it uses the genetic algorithm to search the optimal offloading decision.Experimental results show that compared with the baseline schemes,the proposed algorithm can better improve the resource utilization rate of edges,and reduces the cloud-side communication traffic,and thus redu-cing the potential congestion of the core network.

Key words: Computation offloading, Core network congestion, Mobile edge computing, Resource allocation

CLC Number: 

  • TP393
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