A QoS-Aware Service Optimization Method Based on History Records and Clustering

The number of alternative Web services that provide the same functionality but differ in non-functional characteristics, i.e., Quality of Service (QoS) parameters is more and more larger, and the service providers could not always deliver their “promised” quality. In view of this challenge, we propose a QoS-aware service optimization method based on history records and clustering, named QHRC. In this method, we take advantage of the history records of services´ past performance quality rather than using the tentative QoS values provided by the service providers. And an adaptive hierarchical fuzzy clustering algorithm named H2D-SC algorithm is adopted to cluster the QoS history records for each Web service, then using the centroids of the subclusters to generate QoS history-record based composition plans. Our method aims at ranking the service composition plans by the corresponding QoS history-record based composition plans to select a most qualified service composition plan. At last, we assess the efficiency of the proposed method with an example and experiments.