A proactive approach based on online reliability prediction for adaptation of service-oriented systems.

Service computing is an emerging technology in System of Systems Engineering (SoS Engineering or SoSE), which regards System as a Service (i.e. SaaS), and aims to construct a robust and value-added complex system by outsourcing external component systems through service composition technology. A service-oriented SoS runs under a dynamic and uncertain environment. To successfully deploy SoS’s run-time quality assurance, online reliability time series prediction, which aims to predict the reliability in near future for a service-oriented SoS,arises as a grand challenge in SoS research. In this paper, we tackle the prediction challenge by exploiting two novel prediction models. We adopt motifs-based Dynamic Bayesian Networks (or m_DBNs) model to perform one-step-ahead time series prediction, and propose a multi-steps trajectories DBNs (or multi_DBNs) model to further revise the future reliability prediction. Finally, a proactive adaption strategy is achieved based on the reliability prediction results. Extensive experiments conducted on real-world Web services demonstrate that our models outperform other well-known approaches consistently.