Relative information: theories and applications
Relative information: theories and applications
Rank-deficient and discrete ill-posed problems: numerical aspects of linear inversion
Rank-deficient and discrete ill-posed problems: numerical aspects of linear inversion
Deriving traffic demands for operational IP networks: methodology and experience
IEEE/ACM Transactions on Networking (TON)
Atomic Decomposition by Basis Pursuit
SIAM Review
Measuring ISP topologies with rocketfuel
Proceedings of the 2002 conference on Applications, technologies, architectures, and protocols for computer communications
Traffic matrix estimation: existing techniques and new directions
Proceedings of the 2002 conference on Applications, technologies, architectures, and protocols for computer communications
A case study of OSPF behavior in a large enterprise network
Proceedings of the 2nd ACM SIGCOMM Workshop on Internet measurment
Inferring link weights using end-to-end measurements
Proceedings of the 2nd ACM SIGCOMM Workshop on Internet measurment
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
Traffic engineering with estimated traffic matrices
Proceedings of the 3rd ACM SIGCOMM conference on Internet measurement
NetScope: traffic engineering for IP networks
IEEE Network: The Magazine of Global Internetworking
An independent-connection model for traffic matrices
Proceedings of the 6th ACM SIGCOMM conference on Internet measurement
Packet doppler: network monitoring using packet shift detection
CoNEXT '08 Proceedings of the 2008 ACM CoNEXT Conference
Humpty Dumpty: Putting iBGP Back Together Again
NETWORKING '09 Proceedings of the 8th International IFIP-TC 6 Networking Conference
Spatio-temporal compressive sensing and internet traffic matrices
Proceedings of the ACM SIGCOMM 2009 conference on Data communication
GARCH model-based large-scale IP traffic matrix estimation
IEEE Communications Letters
BGP route prediction within ISPs
Computer Communications
Multi-commodity flow traffic engineering with hybrid MPLS/OSPF routing
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Proceedings of the 6th International COnference
Maximum delay computation for interdomain path selection
International Journal of Network Management
Spatio-temporal compressive sensing and internet traffic matrices
IEEE/ACM Transactions on Networking (TON)
On traffic matrix completion in the internet
Proceedings of the 2012 ACM conference on Internet measurement conference
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Traffic matrices are required inputs for many IP network management tasks, such as capacity planning, traffic engineering, and network reliability analysis. However, it is difficult to measure these matrices directly in large operational IP networks, so there has been recent interest in inferring traffic matrices from link measurements and other more easily measured data. Typically, this inference problem is ill-posed, as it involves significantly more unknowns than data. Experience in many scientific and engineering fields has shown that it is essential to approach such ill-posed problems via "regularization." This paper presents a new approach to traffic matrix estimation using a regularization based on "entropy penalization." Our solution chooses the traffic matrix consistent with the measured data that is information-theoretically closest to a model in which source/destination pairs are stochastically independent. It applies to both point-to-point and point-to-multipoint traffic matrix estimation. We use fast algorithms based on modern convex optimization theory to solve for our traffic matrices. We evaluate our algorithm with real backbone traffic and routing data, and demonstrate that it is fast, accurate, robust, and flexible.