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Collaborative Computing: Networking, Applications and Worksharing. 18th EAI International Conference, CollaborateCom 2022, Hangzhou, China, October 15-16, 2022, Proceedings, Part I

Research Article

Availability-Constrained Application Deployment in Hybrid Cloud-Edge Collaborative Environment

Cite
BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-031-24383-7_13,
        author={Wei Xu and Bing Tang and Feiyan Guo and Xiaoyuan Zhang},
        title={Availability-Constrained Application Deployment in Hybrid Cloud-Edge Collaborative Environment},
        proceedings={Collaborative Computing: Networking, Applications and Worksharing. 18th EAI International Conference, CollaborateCom 2022, Hangzhou, China, October 15-16, 2022, Proceedings, Part I},
        proceedings_a={COLLABORATECOM},
        year={2023},
        month={1},
        keywords={Composite application Microservice deployment Availability Genetic algorithm Cloud-edge collaboration},
        doi={10.1007/978-3-031-24383-7_13}
    }
    
  • Wei Xu
    Bing Tang
    Feiyan Guo
    Xiaoyuan Zhang
    Year: 2023
    Availability-Constrained Application Deployment in Hybrid Cloud-Edge Collaborative Environment
    COLLABORATECOM
    Springer
    DOI: 10.1007/978-3-031-24383-7_13
Wei Xu1, Bing Tang1,*, Feiyan Guo1, Xiaoyuan Zhang1
  • 1: School of Computer Science and Engineering, Hunan University of Science and Technology
*Contact email: btang@hnust.edu.cn

Abstract

Cloud computing offers strong availability and lower cost, while edge computing has lower delay. Deployment of applications by placing microservices in containers in a cloud-edge collaborative environment is adopted by more and more enterprise application providers. For users, they care more about application response time and application availability. For application providers, they also need to save deployment costs to the maximum extent. Therefore, the application deployment in hybrid cloud-edge collaborative environment is a multi-objective optimization problem. In this paper, a genetic algorithm named DP-GA based on improved NSGA-II has been proposed to solve the multi-objective NP-hard problem. We balance the two objectives of minimizing deployment cost and average response time under availability constraints. Using the real dataset of Shanghai Telecom, the experimental results show that the proposed DP-GA is superior to the existing methods, reducing average response time by about 35% and saving deployment cost by about 15%.

Keywords
Composite application Microservice deployment Availability Genetic algorithm Cloud-edge collaboration
Published
2023-01-25
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-24383-7_13
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