Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (2): 300-310.doi: 10.11896/jsjkx.230600128

• Computer Network • Previous Articles     Next Articles

Study on Cache-oriented Dynamic Collaborative Task Migration Technology

ZHAO Xiaoyan1,2, ZHAO Bin1, ZHANG Junna1,2, YUAN Peiyan1   

  1. 1 College of Computer and Information Engineering,Henan Normal University,Xinxiang,Henan 453007,China
    2 Engineering Lab of Intelligence Business&Internet of Things,Xinxiang,Henan 453007,China
  • Received:2023-06-15 Revised:2023-09-26 Online:2024-02-15 Published:2024-02-22
  • About author:ZHAO Xiaoyan,born in 1981,Ph.D,associate professor,is a member of CCF(No.K8282M).Her main research interests include mobile edge computing,D2D communication and Internet of things.ZHANG Junna,born in 1979,Ph.D,associate professor,is a member of CCF(No.D2234M).Her main research interests include mobile edge computing and services computing.
  • Supported by:
    National Natural Science Foundation of China (62072159,61902112) and Science and Technology Research Project of Henan Province(222102210011,232102211061).

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Abstract: Task migration technology has been propelled by the continuous emergence of compute-intensive and delay-sensitive services in edge networks.However,the process of task migration is hindered by technical bottlenecks such as complex and time-varying application scenarios,as well as the high difficulty in problem modeling.Especially when considering user movement,designing a reasonable task migration strategy that ensures the stability and the continuity of user service remains a persistent challenge.Therefore,a mobile-aware service pre-caching model and task pre-migration strategy are proposed to transform the problem of task migration into an optimization problem that combines optimal clustering strategies with edge service pre-caching.First of all,the current state of the task is initially predicted based on the user′s movement trajectory.To solve the problem of when and where to migrate,a pre-migration model for two task scenarios,namely mobile and load,is proposed by introducing the concept of dynamic cooperation cluster and migration prediction radius.And then,according to the tasks that need to be migrated,the maximum tolerant delay constraint is utilized to derive the limit value of cooperative cluster radius and target server quantity in a cluster.Subsequently,a user-centric distributed dynamic multi-server cooperative clustering algorithm(DDMC) and a cache-based double deep Q network algorithm(C-DDQN) for service are proposed to solve the problem of optimal clustering and service ca-ching.Finally,a low-complexity alternate minimization service cache location update algorithm is designed using the causality of service caches to achieve the optimal set of migration target servers,which realize server collaboration and network load balancing in task migration.Experimental results demonstrate the robustness and the system performance of the proposed migration selection algorithm.Compared with other algorithms,the total cost consumed is reduced by at least 12.06%,the total latency consumed is reduced by at least 31.92%.

Key words: Mobile edge computing, Service cache, Dynamic collaborative cluster, Task migration, Deep reinforcement learning

CLC Number: 

  • TP393
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