Approximation algorithms for NP-hard problems
Approximation algorithms for NP-hard problems
Approximation algorithms
Network tomography on general topologies
SIGMETRICS '02 Proceedings of the 2002 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Passive network tomography using Bayesian inference
Proceedings of the 2nd ACM SIGCOMM Workshop on Internet measurment
Robust monitoring of link delays and faults in IP networks
IEEE/ACM Transactions on Networking (TON)
Passive network tomography using EM algorithms
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 03
NetDiagnoser: troubleshooting network unreachabilities using end-to-end probes and routing data
CoNEXT '07 Proceedings of the 2007 ACM CoNEXT conference
Scalable diagnosis in IP networks using path-based measurement and inference: A learning framework
Journal of Visual Communication and Image Representation
Network Tomography of Binary Network Performance Characteristics
IEEE Transactions on Information Theory
Inference of Link Delay in Communication Networks
IEEE Journal on Selected Areas in Communications
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A goal of network tomography is to infer the status (e.g. delay) of congested links internal to a network, through end-to-end measurements at boundary nodes (end-hosts) via insertion of probe signals. Because (a) probing constitutes traffic overhead, and (b) in any typical scenario, the number of congested links is a small fraction of the total number in the network, a desirable design objective is to identify those (few) congested links using a minimum number of probes. In this paper, we make a contribution to solving this problem, by proposing a new two-stage approach for this problem. First, we develop a binary observation model linking end-to-end observations with individual link statuses and derive necessary and sufficient conditions for whether at least one link in the network is congested. Stage I of the proposed method shows that achieving 1-identifiability with a minimum number of probes is equivalent to the familiar minimum set covering problem that can be efficiently solved via a greedy heuristic. A sequential algorithm is described, leading to a significantly lowered computational complexity vis-a-vis a batch algorithm. Next, a binary splitting algorithm originally developed in group testing is used to identify the location of the congested links. The proposed scheme is evaluated by simulations in OPNET and experiments on the PlanetLab testbed to validate the advantages of our 2-stage approach vis-a-vis a conventional (batch) algorithm.