Learning the Evolution Regularities for BigService-Oriented Online Reliability Prediction
Hongbing Wang, Lei Wang, Qi Huang Yu, Zibin Zheng · IEEE Transactions on Services Computing · 2016
Service computing is an emerging technology in System of Systems Engineering (SoS Engineering or SoSE), which regards a System as a Service, and aims at constructing a robust and value-added complex system by outsourcing external component systems through service composition. The burgeoning Big Service computing just covers the significant challenges in constructing and maintaining a stable service-oriented SoS. A service-oriented SoS runs under a volatile and uncertain environment. As a step toward big service, service fault tolerance (FT) can guarantee the run-time quality of a service-oriented SoS. To successfully deploy FT in an SoS, online reliability time series prediction, which aims at predicting the reliability in near future for a service-oriented SoS arises as a grand challenge in SoS research. In particular, we need to tackle a number of big data related issues given the large and fast increasing size of the historical data that will be used for prediction purpose. The decision-making of prediction solution space be more complex. To provide highly accurate prediction results, we tackle the prediction challenges by identifying the evolution regularities of component systems' running states via different machine learning models. We present in this paper the motifs-based Dynamic Bayesian Networks (or m_DBNs) to perform one-step-ahead online reliability time series prediction. We also propose a multi-steps trajectory DBNs (or multi_DBNs) to further improve the accuracy of future reliability prediction. Finally, a Convolutional Neural Networks (CNN)-based prediction approach is developed to deal with the big data challenges. Extensive experiments conducted on real-world Web services demonstrate that our models outperform other well-known approaches consistently.