Traffic matrix estimation: existing techniques and new directions
Proceedings of the 2002 conference on Applications, technologies, architectures, and protocols for computer communications
An information-theoretic approach to traffic matrix estimation
Proceedings of the 2003 conference on Applications, technologies, architectures, and protocols for computer communications
How to identify and estimate the largest traffic matrix elements in a dynamic environment
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
A distributed approach to measure IP traffic matrices
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
Traffic matrix estimation based on a square root Kalman filtering algorithm
International Journal of Network Management
Processing intrusion detection alert aggregates with time series modeling
Information Fusion
Detectability of traffic anomalies in two adjacent networks
PAM'07 Proceedings of the 8th international conference on Passive and active network measurement
On the use of random neural networks for traffic matrix estimation in large-scale IP networks
Proceedings of the 6th International Wireless Communications and Mobile Computing Conference
Computer Networks: The International Journal of Computer and Telecommunications Networking
Online OSPF weights optimization in IP networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
Hi-index | 0.00 |
In this work we develop a new approach to monitoring origin-destination flows in a large network. We start by building a state space model for OD flows that is rich enough to fully capture temporal and spatial correlations. We apply a Kalman filter to our linear dynamic system that can be used for both estimation and prediction of traffic matrices. We call our system a traffic matrix tracker due to its lightweight mechanism for temporal updates that enables tracking traffic matrix dynamics at small time scales. Our Kalman filter approach allows us to go beyond traffic matrix estimation in that our single system can also carry out traffic prediction and yield confidence bounds on the estimates, the predictions and the residual error processes. We show that these elements provide key functionalities needed by monitoring systems of the future for carrying out anomaly detection. Using real data collected from a Tier-1 ISP, we validate our model, illustrate that it can achieve low errors, and that our method is adaptive on both short and long timescales.