A negotiation‐based service selection approach using swarm intelligence and kernel density estimation. (1st March 2018)

• Record Type: Journal Article
• Authors: Mezni, Haithem; Sellami, Mokhtar
• Is Part Of: Software, practice & experience. Volume 48:Number 6(2018)
• Page Start: 1285
• Page End: 1311
• Publication Date: 2018-03-01
• Subjects: kernel density estimation -- negotiation -- particle swarm optimization -- self‐* Web service -- service selection; Computer software -- Periodicals; Computer programming -- Periodicals; Computer programs -- Periodicals; 005.3
• DOI: 10.1002/spe.2575
• Languages: English
• ISSNs: 0038-0644

Abstract

Summary: Nowadays, the cloud computing environment is becoming a natural choice to deploy and provide Web services that meet user needs. However, many services provide the same functionality and high quality of service (QoS) but different self‐adaptive behaviors. In this case, providers' adaptation policies are useful to select services with high QoS and high quality of adaptation (QoA). Existing approaches do not take into account providers' adaptation policies in order to select services with high reputation and high reaction to changes, which is important for the composition of self‐adaptive Web services. In order to actively participate to compositions, candidate services must negotiate their self‐* capabilities. Moreover, they must evaluate the participation constraints against their capabilities specified in terms of QoS and adaptation policies. This paper exploits a variant of particle swarm optimization and kernel density estimation in the selection of service compositions and the concurrent negotiations of their QoS and QoA capabilities. Selection and negotiation processes are held between intelligent agents, which adopt swarm intelligence techniques for achieving optimal selection and optimal agreement on providers' offers. To resolve unknown autonomic behavior of candidate services, we deal with the lack of such information by predicting the real QoA capabilities of a service through the kernel density estimation technique. Experiments show that our solution is efficient in comparison with several state‐of‐the‐art selection approaches.