Maximum likelihood network topology identification from edge-based unicast measurements
SIGMETRICS '02 Proceedings of the 2002 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Merging logical topologies using end-to-end measurements
Proceedings of the 3rd ACM SIGCOMM conference on Internet measurement
Towards capturing representative AS-level Internet topologies
Computer Networks: The International Journal of Computer and Telecommunications Networking
Efficient algorithms for large-scale topology discovery
SIGMETRICS '05 Proceedings of the 2005 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
SIGMETRICS '05 Proceedings of the 2005 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Shrink: a tool for failure diagnosis in IP networks
Proceedings of the 2005 ACM SIGCOMM workshop on Mining network data
A general model for clustering binary data
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
IP fault localization via risk modeling
NSDI'05 Proceedings of the 2nd conference on Symposium on Networked Systems Design & Implementation - Volume 2
A systematic framework for unearthing the missing links: measurements and impact
NSDI'07 Proceedings of the 4th USENIX conference on Networked systems design & implementation
California fault lines: understanding the causes and impact of network failures
Proceedings of the ACM SIGCOMM 2010 conference
Proceedings of the 6th International COnference
A comparison of syslog and IS-IS for network failure analysis
Proceedings of the 2013 conference on Internet measurement conference
| Hi-index | 0.00 |
Health monitoring, automated failure localization and diagnosis have all become critical to service providers of large distribution networks (e.g., digital cable and fiber-to-the-home), due to the increases in scale and complexity of their offered services. Existing automated failure diagnosis solutions typically assume complete knowledge of network topology, which in practice is rarely available. The solution presented in this paper - Network Management and Diagnosis (NetworkMD) - is an automated failure diagnosis system that can infer failure groups based on historical failure data, and optionally geographical information. The inferred failure groups mirror missing topologies, and can be used to localize failures, diagnose root causes of problems, and detect misconfiguration in known topologies. NetworkMD uses an unsupervised learning algorithm based on non-negative matrix factorization (NMF) to infer failure groups. Using cable network as the primary example, we demonstrate the effectiveness of NetworkMD in both simulated settings and real environment using data collected from a commercial network serving hundreds of thousands of customers via thousands of intermediate network devices.