A decentralized collaborative approach to online edge user allocation in edge computing environments

Qinglan Peng, Yunni Xia*, Yan Wang, Chunrong Wu, Wanbo Zheng, Xin Luo, Shanchen Pang, Yong Ma, Chunxu Jiang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

1 Citation (Scopus)

Abstract

Edge computing is a promising paradigm that can boost the performance of novel mobile applications and energize the real-time governance of Internet-of-Things (IoT) big data. In edge computing, mobile application vendors are allowed to employ edge resources to speed up end-users' applications in an elastic and on-demand manner. However, due to the complex geographical distribution of edge servers and users, how to decide the most appropriate destination edge server to hire and how to decide the corresponding user-server allocation plan with as-low-as-possible monetary cost are the key problems for application vendors. Instead of assuming a simultaneous-batch-arrival pattern of incoming users and considering static optimization of the Edge User Allocation (EUA) problem by most existing studies, in this paper, we consider an online EUA problem where users' arrival and departure follow a general pattern. We take the long-term edge user allocation rate and edge server leasing cost as scheduling targets and propose a decentralized collaborative and fuzzy-control-based approach to yielding real-time user-edge-server allocation schedules. In this approach, edge users are allowed to independently make their own allocation decision only based on local information (i.e., the status of nearby edge servers). Experiments on real-world edge datasets demonstrate our approach outperforms state-of-the-art approaches in terms of long-term allocation rate and system cost.
Original languageEnglish
Title of host publication2020 IEEE International Conference on Web Services (IEEE ICWS 2020)
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages294-301
Number of pages8
ISBN (Print)9781728187860
DOIs
Publication statusPublished - 2020
Event2020 IEEE 13th International Conference on Web Services - Virtual
Duration: 18 Oct 202024 Oct 2020

Publication series

NameProceedings - 2020 IEEE 13th International Conference on Web Services, ICWS 2020

Conference

Conference2020 IEEE 13th International Conference on Web Services
Abbreviated titleICWS 2020
Period18/10/2024/10/20

Keywords

  • Edge Computing
  • Online Edge User Allocation
  • Edge Request
  • On-demand instance
  • Fuzzy Control

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