On the self-similar nature of Ethernet traffic (extended version)
IEEE/ACM Transactions on Networking (TON)
Wide area traffic: the failure of Poisson modeling
IEEE/ACM Transactions on Networking (TON)
IEEE/ACM Transactions on Networking (TON)
New models for pseudo self-similar traffic
Performance Evaluation - Special issue on applied probability modelling in telecommunication
Proof of a fundamental result in self-similar traffic modeling
ACM SIGCOMM Computer Communication Review
Use of &agr;-stable self-similar stochastic processes for modeling traffic in broadband networks
Performance Evaluation - Special issue on performance and control of network systems
Modelling the self-similar behaviour of network traffic
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special Issue: performance modeling and evaluation of ATM networks
On the nonstationarity of Internet traffic
Proceedings of the 2001 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Fractional Lévy motion and its applocation to network traffic modeling
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue: Advances in modeling and engineering of Longe-Range dependent traffic
Performance evaluation of the binary logarithmic arbitration method (BLAM)
LCN '96 Proceedings of the 21st Annual IEEE Conference on Local Computer Networks
Criticisms of Modelling Packet Traffic Using Long-Range Dependence
ICCCN '09 Proceedings of the 2009 Proceedings of 18th International Conference on Computer Communications and Networks
A Markovian approach for modeling packet traffic with long-range dependence
IEEE Journal on Selected Areas in Communications
On the use of fractional Brownian motion in the theory of connectionless networks
IEEE Journal on Selected Areas in Communications
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Self-similarity with Long Range Dependence (LRD) in network traffic has been modeled using aggregation from On-Off sources. The model requires that each of the On and Off period distribution follow Power-tail distribution like Pareto distribution. This paper characterizes of burst inter-arrival time of network traffic using Bellcore Morristown Laboratory Data [1] and an Academic Institute LAN trace. It is shown that, LogNormal distribution is a better fit for the data compared to Pareto. Current work analyzes statistically both the data from spatial as well as temporal view point.