A coding approach to event correlation
Proceedings of the fourth international symposium on Integrated network management IV
Multicast-based inference of network-internal delay distributions
IEEE/ACM Transactions on Networking (TON)
Traffic matrix estimation: existing techniques and new directions
Proceedings of the 2002 conference on Applications, technologies, architectures, and protocols for computer communications
Fast accurate computation of large-scale IP traffic matrices from link loads
SIGMETRICS '03 Proceedings of the 2003 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
An information-theoretic approach to traffic matrix estimation
Proceedings of the 2003 conference on Applications, technologies, architectures, and protocols for computer communications
Non-negative Matrix Factorization with Sparseness Constraints
The Journal of Machine Learning Research
NetQuest: a flexible framework for large-scale network measurement
SIGMETRICS '06/Performance '06 Proceedings of the joint international conference on Measurement and modeling of computer systems
IEEE Transactions on Signal Processing
Adaptive diagnosis in distributed systems
IEEE Transactions on Neural Networks
Sparse signal recovery with exponential-family noise
Allerton'09 Proceedings of the 47th annual Allerton conference on Communication, control, and computing
Monitoring abnormal network traffic based on blind source separation approach
Journal of Network and Computer Applications
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We consider the problem of diagnosing performance problems in distributed system and networks given end-to-end performance measurements provided by test transactions, or probes. Common techniques for problem diagnosis such as, for example, codebook and network tomography usually assume a known dependency (e.g., routing) matrix that describes how each probe depends on the systems components. However, collecting full information about routing and/or probe dependencies on all systems components can be very costly, if not impossible, in large-scale, dynamic networks and distributed systems. We propose an approach to problem diagnosis and dependency discovery from end-to-end performance measurements in cases when the dependency/routing information is unknown or partially known. Our method is based on Blind Source Separation (BSS) approach that aims at reconstructing unobserved input signals and the mixing-weights matrix from the observed mixtures of signals. Particularly, we apply sparse non-negative matrix factorization techniques that appear particularly fitted to the problem of recovering network bottlenecks and dependency (routing) matrix, and show promising experimental results on several realistic network topologies.