Estimation of the self-similarity parameter in linear fractional stable motion
Signal Processing - Signal processing with heavy-tailed models
Singular spectrum analysis of traffic workload in a large-scale wireless lan
Proceedings of the 10th ACM Symposium on Modeling, analysis, and simulation of wireless and mobile systems
LASS: a tool for the local analysis of self-similarity
Computational Statistics & Data Analysis
Invariances, Laplacian-like wavelet bases, and the whitening of fractal processes
IEEE Transactions on Image Processing
Transition from heavy to light tails in retransmission durations
INFOCOM'10 Proceedings of the 29th conference on Information communications
Modulated Branching Processes, Origins of Power Laws, and Queueing Duality
Mathematics of Operations Research
Wireless Personal Communications: An International Journal
The multi-fractal nature of worm and normal traffic at individual source level
ISI'05 Proceedings of the 2005 IEEE international conference on Intelligence and Security Informatics
Difficulties in modeling SCADA traffic: a comparative analysis
PAM'12 Proceedings of the 13th international conference on Passive and Active Measurement
Retransmission Delays With Bounded Packets: Power-Law Body and Exponential Tail
IEEE/ACM Transactions on Networking (TON)
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We demonstrate that Ethernet local area network (LAN) traffic is statistically self-similar, that none of the commonly used traffic models is able to capture this fractal behavior, and that such behavior has serious implications for the design, control, and analysis of high-speed, cell-based networks. Intuitively, the critical characteristic of this self-similar traffic is that there is no natural length of a "burst": at every time scale ranging from a few milliseconds to minutes and hours, similar-looking traffic bursts are evident; we find that aggregating streams of such traffic typically intensifies the self-similarity ("burstiness") instead of smoothing it.Our conclusions are supported by a rigorous statistical analysis of hundreds of millions of high quality Ethernet traffic measurements collected between 1989 and 1992, coupled with a discussion of the underlying mathematical and statistical properties of self-similarity and their relationship with actual network behavior. We also consider some implications for congestion control in high-bandwidth networks and present traffic models based on self-similar stochastic processes that are simple, accurate, and realistic for aggregate traffic.