Online deployment algorithms for microservice systems with Ccomplex dependencies

Xiang He, Zhiying Tu, Markus Wagner, Xiaofei Xu, Zhongjie Wang

Research output: Contribution to journalArticleResearchpeer-review

24 Citations (Scopus)

Abstract

Cloud and edge computing have been widely adopted in many application scenarios. With the increasing demand of fast iteration and complexity of business logic, it is challenging to achieve rapid development and continuous delivery in such highly distributed cloud and edge computing environment. At present, microservice-based architecture has been the dominant deployment style, and a microservice system has to evolve agilely to offer stable Quality of Service (QoS) in the situation where user requirement changes frequently. Many research have been conducted to optimally re-deploy microservices to adapt to changing requirements. Nevertheless, complex dependencies between microservices and the existence of multiple instances of one single microservice in a microservice system have not been fully considered in existing works. This paper defines SPPMS, the Service Placement Problem in Microservice Systems that feature complex dependencies and multiple instances, as a Fractional Polynomial Problem (FPP) . Considering the high computation complexity of FPP, it is then transformed into a Quadratic Sum-of-Ratios Fractional Problem (QSRFP) which is further solved by the proposed greedy-based algorithms. Experiments demonstrate that our models and algorithms outperform existing approaches in both quality and computation speed.

Original languageEnglish
Pages (from-to)1746-1763
Number of pages18
JournalIEEE Transactions on Cloud Computing
Volume11
Issue number2
DOIs
Publication statusPublished - 1 Apr 2023
Externally publishedYes

Keywords

  • Cloud computing
  • Cloud Computing
  • Microservice architectures
  • Microservice Systems
  • Multiple Instance Coexistence
  • Production facilities
  • Quality of service
  • Servers
  • Service Dependencies
  • Service Placement
  • Task analysis
  • Time factors

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