Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
Probabilistic fault localization in communication systems using belief networks
IEEE/ACM Transactions on Networking (TON)
Shrink: a tool for failure diagnosis in IP networks
Proceedings of the 2005 ACM SIGCOMM workshop on Mining network data
Probabilistic fault diagnosis for IT services in noisy and dynamic environments
IM'09 Proceedings of the 11th IFIP/IEEE international conference on Symposium on Integrated Network Management
Probabilistic fault diagnosis using adaptive probing
DSOM'07 Proceedings of the Distributed systems: operations and management 18th IFIP/IEEE international conference on Managing virtualization of networks and services
Tractable inference for complex stochastic processes
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Hi-index | 0.01 |
In order to improve the quality of Internet service, it is important to quickly and accurately diagnose the root fault from the observed symptoms and knowledge. Because of the dynamical changes in service system which are caused by many factors such as dynamic routing and link congestion, the dependence between the observed symptoms and the root faults becomes more complex and uncertain, especially in noisy environment. Therefore, the performance of fault localization based on static Bayesian network (BN) degrades. This paper establishes a fault diagnosis technique based on dynamic Bayesian network (DBN), which can deal with the system dynamics and noise. Moreover, our algorithm has taken several measures to reduce the algorithm complexity in order to run efficiently in large-scale networks. We implement simulation and compare our algorithm with our former algorithm based on BN (ITFD) in accuracy, efficiency and time. The results show that our algorithm can be effectively used to diagnose the root fault in high-level applications.