Semi-empirical service composition: A clustering based approach

Service composition has the capability of constructing coarse-grained solutions by dynamically aggregating a set of services to satisfy complex requirements, but it suffers from dramatic decrease on the efficiency of determining the best composition solution when large scale candidate services are available. Most current approaches look for the optimal composition solution by real-time computation, and the composition efficiency greatly depends on the adopted algorithms. To eliminate such deficiency, this paper proposes a semi-empirical composition approach which incorporates two stages, i.e., periodical clustering and real-time composition. The former partitions the candidate services and historical requirements into clusters based on similarity measurement, and then the probabilistic correspondences between service clusters and requirement clusters are identified by statistical analysis. The latter deals with a new requirement by firstly finding its most similar requirement cluster and the corresponding service clusters by leveraging Bayesian inference, then a set of concrete services are optimally selected from such reduced solution space and constitute the final composition solution. Instead of relying on solely historical data exploration or on pure real-time computation, our approach distinguishes from traditional methods by combining the two perspectives together. Experiments demonstrate the advantages of this approach.