Multicast-based inference of network-internal delay distributions
IEEE/ACM Transactions on Networking (TON)
Mini-buckets: A general scheme for bounded inference
Journal of the ACM (JACM)
Bucket elimination: a unifying framework for probabilistic inference
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
IEEE Transactions on Signal Processing
Multicast-based inference of network-internal loss characteristics
IEEE Transactions on Information Theory
Adaptive diagnosis in distributed systems
IEEE Transactions on Neural Networks
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In this paper, we address network fault diagnosis using multicast-based probing where probes are susceptible to measurement errors. The problem is inspired by and relevant to multicast-based IPTV services being heavily deployed by telecom operators around the world. Specifically, we extend the "noiseless" disjunctive fault model for multicast-based probing presented in [1] to include measurement errors, and derive key results concerning the most probable fault scenario and the most likely fault given the observed probe values. We show that the generalization to include measurement errors adds a fixed amount of overhead per computational step. These results provide a basis, in the face of potential probe measurement errors, for efficient computational procedures to determine the nodes that are most likely to have been in a faulty state and can be used as part of test strategies for network fault diagnosis. Our procedures exploit the underlying structure of the multicast tree and are significantly more efficient than generic computational procedures for probabilistic inference. They can form the basis for accurate and timely fault diagnosis and service quality resolution procedures as part of performance management and customer care systems respectively.