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

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 gradua...

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Bibliographic Details
Published inJi suan ji ke xue Vol. 48; no. 3; p. 281
Main Authors Li, Zhen-Jiang, Zhang, Xing-Lin
Format Journal Article
LanguageChinese
Published Chongqing Guojia Kexue Jishu Bu 01.01.2021
Subjects
Algorithms
Cloud computing
Communications traffic
Computation offloading
Edge computing
Genetic algorithms
Internet of Things
Internet of Vehicles
Microprocessors
Resource allocation
Resource utilization
Smart buildings
Traffic congestion
Traffic control
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Summary: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 impro
ISSN:1002-137X