Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (10): 275-281.doi: 10.11896/jsjkx.220900185

• Computer Network • Previous Articles     Next Articles

Cost-minimizing Task Offload Strategy for Mobile Devices Under Service Cache Constraint

ZHANG Junna1,2, CHEN Jiawei1, BAO Xiang1, LIU Chunhong1, YUAN Peiyan1   

  1. 1 School of Computer and Information Engineering,Henan Normal University,Xinxiang,Henan 453007,China
    2 Engineering Lab of Intelligence Business & Internet of Things,Henan Normal University,Xinxiang,Henan 453007,China
  • Received:2022-09-17 Revised:2022-12-06 Online:2023-10-10 Published:2023-10-10
  • About author:ZHANG Junna,born in 1979,Ph.D,associate professor,is a member of China Computer Federation.Her main research interests include edge computing and service computing.CHEN Jiawei,born in 1998,postgra-duate,is a member of China Computer Federation.His main research interests include edge computing and service computing.
  • Supported by:
    National Natural Science of China(61902112,62072159) and Guangxi Key Laboratory of Cryptography and Information Security(GCIS202115).

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Abstract: Edge computing provides more computing and storage capabilities at the edge of the network to effectively reduce execution delay and power consumption of mobile devices.Since applications consume more and more computing and storage resources,task offloading has become one of effective solutions to address the inherent limitations in mobile terminals.However,existing researches on task offloading often ignore the diversity of service requirements for different types of tasks and that edge servers have limited services capabilities,resulting in infeasible offloading decisions.Therefore,we study the task offloading pro-blem that can optimize the execution cost of mobile devices under service cache constraints.We first design a collaborative offloa-ding model integrated remote cloud,edge server and local device to balance the load of edge server.Meanwhile,cloud server is used to make up for the limited-service caching capacity of the edge server.Secondly,a task offloading algorithm suitable for cloud-edge-device collaboration is proposed to optimize the execution delay and energy cost of mobile devices.When the task is offloaded,the improved greedy algorithm is used to select the best edge server.Then,the offload decision of the task is determined by comparing the execution cost of the task at different locations.Experimental results show that the proposed algorithm can effectively reduce the execution cost of mobile devices compared with the comparison algorithms.

Key words: Edge computing, Task offloading, Cloud-Edge-Device collaboration, Service caching, Offloading strategy optimization

CLC Number: 

  • TP302
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