Clustering-based and QoS-aware services composition algorithm for ambient intelligence - Khanouche, Mohamed Essaid; Attal, Ferhat; Amirat, Yacine; Chibani, Abdelghani; Kerkar, Moussa (Information sciences, 2019)
Due to the dynamic nature of ubiquitous computing and ambient intelligence (AmI) environments, a challenging issue that needs to be addressed is how to construct composite services that satisfy users’ requirements in terms of quality of service (QoS). In this paper, a clustering-based and QoS-aware services composition algorithm (CQCA) is proposed. To increase the composition optimality and reduce the composition time, the candidate services are first partitioned into clusters, where each cluster represents a QoS level. In addition, a new formulation of the utility function based on the use of the characteristics of the resulting clusters is proposed to remove unpromising candidate services in terms of QoS. A lexicographic optimization method is then exploited to filter out candidate services that have low QoS attributes values. Finally, a search tree is constructed to find near-to-optimal compositions. The obtained performance shows that the proposed algorithm outperforms other composition approaches by finding very near-to-optimal compositions in a reduced composition time.