Correlation-Supported Composite Service Reselection

Reselection of composition service is one of the core research issues in the service computing field. Most of the existing researches for this problem are based on the assumption that the tasks involved are independent. However, in practical scenar-ios, the QoS of some candidate services have correlations with other services, which makes the corresponding tasks be correlated with each other. This leads the QoS used to determine the binding relationship between tasks and concrete services to be inaccurate, so the reselected composite service is not the optimal one in the real executing environment for these existing reselection methods. To address this problem, this paper considers task correlations for runtime rebinding. Firstly, the QoS dependencies among services are extracted from the log repository through the APRIORI data mining method. Then, the acquired QoS dependencies are mapped to the tasks correlations by the defined mapping rules. Finally, the reselection process is implemented by making the tasks which have related relationships as a task unit, and the related services of each task unit as its candidate service set. The effectiveness of this approach, in terms of time and quality, is demonstrated via experiments.