Wide area network design: concepts and tools for optimization
Wide area network design: concepts and tools for optimization
Deriving traffic demands for operational IP networks: methodology and experience
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
A signal analysis of network traffic anomalies
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
Structural analysis of network traffic flows
Proceedings of the joint international conference on Measurement and modeling of computer systems
Diagnosing network-wide traffic anomalies
Proceedings of the 2004 conference on Applications, technologies, architectures, and protocols for computer communications
Modeling distances in large-scale networks by matrix factorization
Proceedings of the 4th ACM SIGCOMM conference on Internet measurement
Traffic matrices: balancing measurements, inference and modeling
SIGMETRICS '05 Proceedings of the 2005 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
A first step toward understanding inter-domain routing dynamics
Proceedings of the 2005 ACM SIGCOMM workshop on Mining network data
Simplifying the synthesis of internet traffic matrices
ACM SIGCOMM Computer Communication Review
IEEE/ACM Transactions on Networking (TON)
Providing public intradomain traffic matrices to the research community
ACM SIGCOMM Computer Communication Review
Robust traffic matrix estimation with imperfect information: making use of multiple data sources
SIGMETRICS '06/Performance '06 Proceedings of the joint international conference on Measurement and modeling of computer systems
An independent-connection model for traffic matrices
Proceedings of the 6th ACM SIGCOMM conference on Internet measurement
An empirical approach to modeling inter-AS traffic matrices
IMC '05 Proceedings of the 5th ACM SIGCOMM conference on Internet Measurement
IMC '05 Proceedings of the 5th ACM SIGCOMM conference on Internet Measurement
Sensitivity of PCA for traffic anomaly detection
Proceedings of the 2007 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Spatio-temporal compressive sensing and internet traffic matrices
Proceedings of the ACM SIGCOMM 2009 conference on Data communication
The nature of data center traffic: measurements & analysis
Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference
Exact Matrix Completion via Convex Optimization
Foundations of Computational Mathematics
BasisDetect: a model-based network event detection framework
IMC '10 Proceedings of the 10th ACM SIGCOMM conference on Internet measurement
Proceedings of the 6th International COnference
A case study of the accuracy of SNMP measurements
Journal of Electrical and Computer Engineering
IEEE Transactions on Information Theory
Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
IEEE Transactions on Information Theory
Optimizing OSPF/IS-IS weights in a changing world
IEEE Journal on Selected Areas in Communications
Self and static interference mitigation scheme for coexisting wireless networks
Computers and Electrical Engineering
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Despite advances in measurement technology, it is still challenging to reliably compile large-scale network datasets. For example, because of flaws in the measurement systems or difficulties posed by the measurement problem itself, missing, ambiguous, or indirect data are common. In the case where such data have spatio-temporal structure, it is natural to try to leverage this structure to deal with the challenges posed by the problematic nature of the data. Our work involving network datasets draws on ideas from the area of compressive sensing and matrix completion, where sparsity is exploited in estimating quantities of interest. However, the standard results on compressive sensing are: 1) reliant on conditions that generally do not hold for network datasets; and 2) do not allow us to exploit all we know about their spatio-temporal structure. In this paper, we overcome these limitations with an algorithm that has at its heart the same ideas espoused in compressive sensing, but adapted to the problem of network datasets. We show how this algorithm can be used in a variety of ways, in particular on traffic data, to solve problems such as simple interpolation of missing values, traffic matrix inference from link data, prediction, and anomaly detection. The elegance of the approach lies in the fact that it unifies all of these tasks and allows themto be performed even when as much as 98% of the data is missing.