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
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
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
The problem of synthetically generating IP traffic matrices: initial recommendations
ACM SIGCOMM Computer Communication Review
Traffic engineering with traditional IP routing protocols
IEEE Communications Magazine
An independent-connection model for traffic matrices
Proceedings of the 6th ACM SIGCOMM conference on Internet measurement
On the predictive power of shortest-path weight inference
Proceedings of the 8th ACM SIGCOMM conference on Internet measurement
On basic properties of fault-tolerant multi-topology routing
Computer Networks: The International Journal of Computer and Telecommunications Networking
GATEway: symbiotic inter-domain traffic engineering
Proceedings of the 3rd International Conference on Performance Evaluation Methodologies and Tools
A note on simulation of LRD network traffic
IMCAS'09 Proceedings of the 8th WSEAS international conference on Instrumentation, measurement, circuits and systems
Spatio-temporal compressive sensing and internet traffic matrices
Proceedings of the ACM SIGCOMM 2009 conference on Data communication
Loop-free alternates and not-via addresses: A proper combination for IP fast reroute?
Computer Networks: The International Journal of Computer and Telecommunications Networking
Joint time-frequency sparse estimation of large-scale network traffic
Computer Networks: The International Journal of Computer and Telecommunications Networking
Proceedings of the 23rd International Teletraffic Congress
Effectiveness of link cost optimization for IP rerouting and IP fast reroute
MMB&DFT'10 Proceedings of the 15th international GI/ITG conference on Measurement, Modelling, and Evaluation of Computing Systems and Dependability and Fault Tolerance
Design and implementation of a consolidated middlebox architecture
NSDI'12 Proceedings of the 9th USENIX conference on Networked Systems Design and Implementation
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
New opportunities for load balancing in network-wide intrusion detection systems
Proceedings of the 8th international conference on Emerging networking experiments and technologies
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A recent paper [8] presented methods for several steps along the road to synthesis of realistic traffic matrices. Such synthesis is needed because traffic matrices are a crucial input for testing many new networking algorithms, but traffic matrices themselves are generally kept secret by providers. Furthermore, even given traffic matrices from a real network, it is difficult to realistically adjust these to generate a range of scenarios (for instance for different network sizes). This note is concerned with the first step presented in [8]: generation of a matrix with similar statistics to that of a real traffic matrix. The method applied in [8] is based on fitting a large number of distributions, and finding that the log-normal distribution appears to fit most consistently. Best fits (without some intuitive explanation for the fit) are fraught with problems. How general are the results? How do the distribution parameters relate? This note presents a simpler approach based on a gravity model. Its simplicity provides us with a better understanding of the origins of the results of [8], and this insight is useful, particularly because it allows one to adapt the synthesis process to different scenarios in a more intuitive manner. Additionally, [8] measures the quality of its fit to the distribution's body. This note shows that the tails of the distributions are less heavy than the log-normal distribution (a counterintuitive result for Internet traffic), and that the gravity model replicates these tails more accurately.