Quality Failure Prediction for the Self-Healing of Service-Oriented System of Systems

With software systems increasingly being employed in more complex and critical contexts, service-oriented system of systems has been paid more and more attention as a novel software system structure, which considers System as a Service. Under the loosely coupled SoS´s dynamic and uncertain running environment, self-healing process, as the important safeguard mechanism of system running, pose a great threat to system quality analysis. Particularly, as the first step of self-healing, the research of quality failure prediction faces not only continual and immediate disturbance on service quality, but also complex users´ preference on quality. In this paper, we propose a model based on Stochastic Differential Equations to analyze the disturbance more precisely and dynamically. And we adopt weighted conditional preference to consider different users´ requirements. This model is testified in an empirical case study, in which the real data set is collected in real-time from the system platform of a Telecom in China. The experiments verify model´s prediction abilities and evaluate the impact of the parameters on the prediction accuracy.