Information Sciences: an International Journal
Efficient active probing for fault diagnosis in large scale and noisy networks
INFOCOM'10 Proceedings of the 29th conference on Information communications
Preprocessing expert system for mining association rules in telecommunication networks
Expert Systems with Applications: An International Journal
First step towards automatic correction of firewall policy faults
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
Inference of network anomaly propagation using spatio-temporal correlation
Journal of Network and Computer Applications
Estimation of the available bandwidth ratio of a remote link or path segments
Computer Networks: The International Journal of Computer and Telecommunications Networking
Fine-grain diagnosis of overlay performance anomalies using end-point network experiences
Proceedings of the 8th International Conference on Network and Service Management
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Fault localization is the core element in fault management. Symptom-fault map is commonly used to describe the symptom-fault causality in fault reasoning. For Internet service networks, a well-designed monitoring system can effectively correlate the observable symptoms (i.e., alarms) with the critical network faults (e.g., link failure). However, the lost and spurious symptoms can significantly degrade the performance and accuracy of a passive fault localization system. For overlay networks, due to limited underlying network accessibility, as well as the overlay scalability and dynamics, it is impractical to build a static overlay symptom-fault map. In this paper, we firstly propose a novel active integrated fault reasoning (AIR) framework to incrementally incorporate active investigation actions into the passive fault reasoning process based on an extended symptom-fault-action (SFA) model. Secondly, we propose an overlay network profile (ONP) to facilitate the dynamic creation of an overlay symptom-fault-action (called O-SFA) model, such that the AIR framework can be applied seamlessly to overlay networks (called O-AIR). As a result, the corresponding fault reasoning and action selection algorithms are elaborated. Extensive simulations and Internet experiments show that AIR and O-AIR can significantly improve both accuracy and performance in the fault reasoning for Internet and overlay service networks, especially when the ratio of the lost and spurious symptoms is high.