On the self-similar nature of Ethernet traffic (extended version)
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
Time Series Models for Internet Data Traffic
LCN '99 Proceedings of the 24th Annual IEEE Conference on Local Computer Networks
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
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
Time series models for internet traffic
INFOCOM'96 Proceedings of the Fifteenth annual joint conference of the IEEE computer and communications societies conference on The conference on computer communications - Volume 2
A Methodology of Traffic Engineering to IP Backbone
IPOM '09 Proceedings of the 9th IEEE International Workshop on IP Operations and Management
Topology aware internet traffic forecasting using neural networks
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
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Network planning is usually based on long-term trends and forecasts of Internet traffic. However, between two large updates, telecommunication operators deal with resource allocation in contracts depending on the mid-term evolution of their own traffic. In this paper, we develop a methodology to forecast the fluctuations of Internet traffic in an international IP transit network. We do not work on traffic demands which can not be easily measured in a large network. Instead, we use link counts which are much simpler to obtain. If needed, the origin-destination demands are estimated a posteriori through traffic matrix inference techniques. We analyze link counts stemming from France Telecom IP international transit network at the two hours time scale over nineteen weeks and produce forecasts for five weeks (mid-term). Our methodology relies on Principal Component Analysis and time series modeling taking into account the strain of cycles. We show that five components represent 64% of the traffic total variance and that these components are quite stable over time. This stability allows us to develop a method that produce forecasts automatically without any model to fit.