Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (6): 336-345.doi: 10.11896/jsjkx.240400073

• Computer Network • Previous Articles     Next Articles

Resource Allocation Method with Workload-time Windows for Serverless Applications inCloud-edge Collaborative Environment

ZHANG Minghao1,2, XIAO Bohuai1,2, ZHENG Song3,4, CHEN Xing1,2   

  1. 1 College of Computer and Data Science,Fuzhou University,Fuzhou 350108,China
    2 Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing,Fuzhou 350108,China
    3 College of Electrical Engineering and Automation,Fuzhou University,Fuzhou 350108,China
    4 Key Laboratory of Industrial Automation Control Technology and Information Processing(Fuzhou University) of Fujian Province,Fuzhou 350108,China
  • Received:2024-04-11 Revised:2024-08-12 Online:2025-06-15 Published:2025-06-11
  • About author:ZHANG Minghao,born in 1998,postgraduate.His main research interests include cloud computing,resource allocation and serverless computing.
    CHEN Xing,born in 1985,Ph.D,professor,Ph.D supervisor,is a distinguished member of CCF(No.35725M).His main research interests include software engineering,systems software and cloud computing.
  • Supported by:
    National Natural Science Foundation of China(62072108),Science and Technology and Economic Integration Service Platform of Fujian Province(2023XRH001),Collaborative Innovation Platform Project of Fu Xia Quan National Independent Innovation Demonstration Area(2022FX5) and Special Funds for Promoting the High-quality Development of Marine and Fishery Industry of Fujian Province(FJHYF-ZH-2023-02).

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Abstract: With the increasingly diverse computational demands in the cloud-edge collaborative environment,the traditional computing architecture based on virtual machines as the smallest unit of resources exhibits inflexibility and low cost-effectiveness.Serverless computing,as an emerging computing architecture with excellent scalability and flexibility,provides a new perspective to address these issues.In response to the resource allocation problem with workload-time windows for serverless applications in cloud-edge collaborative environments,this study proposes a resource allocation method based on rule-driven co-evolution algorithm(RARCA).This method considers the workload at a certain resource adjustment moment and in the foreseeable future,employing a rule-driven distributed resource updating mechanism to achieve dynamic allocation and adjustment of computing resources.Additionally,by leveraging the information sharing and cooperative optimization capabilities of the co-evolution mechanism,the algorithm efficiently searches for globally optimal resource allocation solutions,significantly improving the real-time and effectiveness of the overall resource allocation method.Experimental results demonstrate that RARCA can achieve superior resource allocation solutions with decision times in seconds,outperforming baseline methods by 2.8% to 14.5% in resource allocation performance.

Key words: Cloud-edge collaborative, Resource allocation, Workload-time windows, Serverless computing, Co-evolutionary algorithm

CLC Number: 

  • TP393
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