Network-based heuristics for constraint-satisfaction problems
Artificial Intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Elements of information theory
Elements of information theory
Network management: a practical perspective
Network management: a practical perspective
Schemes for fault identification in communication networks
IEEE/ACM Transactions on Networking (TON)
A coding approach to event correlation
Proceedings of the fourth international symposium on Integrated network management IV
A revolution: belief propagation in graphs with cycles
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
A Tractable Inference Algorithm for Diagnosing Multiple Diseases
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
Proactive Network Fault Detection
INFOCOM '97 Proceedings of the INFOCOM '97. Sixteenth Annual Joint Conference of the IEEE Computer and Communications Societies. Driving the Information Revolution
Efficient reasoning in graphical models
Efficient reasoning in graphical models
System Fault Diagnosis: Closure and Diagnosability with Repair
IEEE Transactions on Computers
Empirical evaluation of approximation algorithms for probabilistic decoding
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
A scheme for approximating probabilistic inference
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Why is diagnosis using belief networks insensitive to imprecision in probabilities?
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Probabilistic fault diagnosis in communication systems through incremental hypothesis updating
Computer Networks: The International Journal of Computer and Telecommunications Networking
Automatic misconfiguration troubleshooting with peerpressure
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
Call Forwarding-Based Active Probing for POTS Fault Isolation
Journal of Network and Systems Management
Dynamic active probing of helpdesk databases
Proceedings of the VLDB Endowment
A Novel Fault Diagnosis Approach to Path-Protected WDM Mesh Networks
APNOMS '08 Proceedings of the 11th Asia-Pacific Symposium on Network Operations and Management: Challenges for Next Generation Network Operations and Service Management
Active Diagnosis of High-Level Faults in Distributed Internet Services
APNOMS '08 Proceedings of the 11th Asia-Pacific Symposium on Network Operations and Management: Challenges for Next Generation Network Operations and Service Management
On the design of social diagnosis algorithms for multi-agent teams
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Scalable diagnosis in IP networks using path-based measurement and inference: A learning framework
Journal of Visual Communication and Image Representation
PeerWatch: a fault detection and diagnosis tool for virtualized consolidation systems
Proceedings of the 7th international conference on Autonomic computing
Causal networks for risk and compliance: methodology and application
IBM Journal of Research and Development
Prioritizing tests for fault localization through ambiguity group reduction
ASE '11 Proceedings of the 2011 26th IEEE/ACM International Conference on Automated Software Engineering
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We consider the use of probing technology for cost-effective fault diagnosis in computer networks. Probes are test transactions that can be actively selected and sent through the network. This work addresses the probing problem using methods from artificial intelligence. We call the resulting approach intelligent probing. The probes are selected by reasoning about the interactions between the probe paths. Although finding the optimal probe set is prohibitively expensive for large networks, we implement algorithms that find near-optimal probe sets in linear time. In the diagnosis phase, we use a Bayesian network approach and use a local-inference approximation scheme that avoids the intractability of exact inference for large networks. Our results show that the quality of this approximate inference "degrades gracefully" under increasing uncertainty and increases as the quality of the probe set increases.