Efficient Correlation-Aware Service Selection

Abstract

Accounting for quality correlations among web services when performing service composition is essential to obtain more accurate quality estimations of service combinations, thus providing users with better composite solutions. Yet, most current composition approaches fail to address such correlations by assuming independence between services regarding their quality values. In response, this paper presents a correlation-aware composition approach, where quality dependencies among services are modelled and considered during composite service selection. Moreover, to improve selection efficiency, correlation-aware search space reduction techniques are introduced, which prune out uninteresting service compositions prior to selection. The effectiveness of the approach, in terms of time and optimality, is demonstrated via experimental results.