Efficient QoS-aware service recommendation for multi-tenant service-based systems in cloud

Yanchun Wang, Qiang He, Xuyun Zhang, Dayong Ye, Yun Yang

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)


The popularity of cloud computing has fueled the growth in multi-tenant service-based systems (SBSs) that are composed of selected cloud services. In the cloud environment, a multi-tenant SBS simultaneously serves multiple tenants that usually have differentiated QoS requirements. This unique characteristic further complicates the problems of QoS-aware service selection at build-time and system adaptation at runtime, and renders conventional approaches obsolete and inefficient. In the dynamic and volatile cloud environment, the efficiency of building and adapting a multi-tenant SBS is of paramount importance. In this paper, we present two service recommendation approaches for multi-tenant SBSs, one for build-time and one for runtime, based on K-Means clustering and Locality-Sensitive Hashing (LSH) techniques respectively, aiming at finding appropriate services efficiently. Extensive experimental results demonstrate that our approaches can facilitate fast multi-tenant SBS construction and rapid system adaptation.
Original languageEnglish
Pages (from-to)1045-1058
Number of pages14
JournalIEEE Transactions on Services Computing
Issue number6
Early online date10 Oct 2017
Publication statusPublished - Nov 2020
Externally publishedYes


  • Cloud computing
  • Cloud Computing
  • Clustering
  • Quality of service
  • Quality of Service
  • Runtime
  • Service Recommendation
  • Service-Based System
  • Streaming media
  • System Adaptation
  • quality of service
  • service-based system
  • service recommendation
  • system adaptation
  • clustering


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