Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (9): 319-330.doi: 10.11896/jsjkx.240200036

• Computer Network • Previous Articles     Next Articles

Study on Adaptive Cloud-Edge Collaborative Scheduling Methods for Multi-object State Perception

ZHOU Wenhui, PENG Qinghua, XIE Lei   

  1. State Key Laboratory for Novel Software Technology,Nanjing University,Nanjing 210000,China
  • Received:2024-02-05 Revised:2024-06-07 Online:2024-09-15 Published:2024-09-10
  • About author:ZHOU Wenhui,born in 2000,postgra-duate,is a student member of CCF(No.E0259G).His main research interests include edge computing and edge intelligence.
    XIE Lei,born in 1982,Ph.D,professor,Ph.D supervisor,Young Chang Jiang Scholar,is a distinguished member of CCF(No.17652D).His main research interests include wireless sensing,wea-rable computing and edge computing.
  • Supported by:
    National Key R&D Program of China(2022YFB3303900) and National Natural Science Foundation of China(62272216).

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Abstract: With the development of smart cities and intelligent industrial manufacturing,the demand for comprehensive information from surveillance cameras for multi-objective visual analysis has become increasingly prominent.Existing research mainly focuses on resource scheduling on servers and improvements of visual model,which often struggle to adequately handle dynamic changes in system resource and task state.With the advancement of edge hardware resources and task processing models,designing an adaptive cloud-edge collaborative scheduling model to meet the real-time user requirements of tasks has become an essential approach to optimize multi-objective state perception tasks.Thus,based on a profound analysis of characteristics of multi-objective state perception tasks in cloud-edge scenarios,this paper proposes a model of adaptive task scheduler based on soft actor-critic(ATS-SAC).ATS-SAC intelligently decides key factors of tasks such as video stream configuration and model deployment configuration according to real-time analysis of runtime state,thereby significantly optimizing the accuracy and delay of multi-objective state perception tasks in cloud-edge scenarios.Furthermore,we introduce an action filtering method based on user expe-rience threshold that helps to eliminate redundant decision-making actions,so as to reduce the decision-making space of the mo-del.Depending on user's varied demands for performance outcomes of the multi-objective state perception tasks,ATS-SAC model can provide three flexible scheduling strategies,namely speed mode,balance mode,and precision mode.Experimental results show that,comparing to other executing methods,the scheduling strategies of ATS-SAC mo-del make multi-objective state perception tasks more satisfactory in terms of accuracy and delay.Moreover,when the real-time operating state changes,the ATS-SAC mo-del can dynamically adjust its scheduling strategies to maintain stable task processing results.

Key words: Edge computing, Cloud-Edge collaboration, Scheduling policy, Multi-object state perception, Deep reinforcement learning

CLC Number: 

  • TP393
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