Online Reliability Prediction via Motifs-Based Dynamic Bayesian Networks for Service-Oriented Systems

A service-oriented System of Systems (SoS) considers a system as a service and constructs a robust and value-added SoS by outsourcing external component systems through service composition techniques. Online reliability prediction for the component systems for the purpose of assuring the overall Quality of Service (QoS) is often a major challenge in coping with a loosely coupled SoS operating under dynamic and uncertain running environments. It is also a prerequisite for guaranteeing runtime QoS of a SoS through optimal service selection for reliable system construction. We propose a novel online reliability time series prediction approach for the component systems in a service-oriented SoS. We utilize Probabilistic Graphical Models (PGMs) to yield near-future, time series predictions. We assess the approach via invocation records collected from widely used real Web services. Experimental results have confirmed the effectiveness of the approach.