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 journalArticle

6 Citations (Scopus)

Abstract

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
JournalIEEE Transactions on Services Computing
DOIs
Publication statusE-pub ahead of print - 10 Oct 2017
Externally publishedYes

Keywords

  • Cloud computing
  • Cloud Computing
  • Clustering
  • Quality of service
  • Quality of Service
  • Runtime
  • Service Recommendation
  • Service-Based System
  • Streaming media
  • System Adaptation

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