Upcoming Conferences
"Heavy Tail Analysis and Network Modeling"
Sidney Resnick, Cornell University
http://www.orie.cornell.edu/orie/people/faculty/profile.cfm?netid=sir1
Data networks can be analyzed at large time scales (Mikosch et al., 2002; Resnick, 2006) or small time scales (D'Auria and Resnick, 2006, 2007) with time scaling either approaching 1 or 0. One can try to characterize multi-user input traffic or single user inputs (Mikosch and Resnick, 2006). Tails of payload per session can be heavy with Pareto parameter in the interval (1, 2) as in Mikosch et al., 2002 or D'Auria and Resnick, 2006, 2007 or even so heavy that the mean is infinite as in Mikosch and Resnick, 2006 and Resnick and Rootzen, 2000. One sees the impact of stable and Levy processes in various ways in all these circumstances depending on interaction of heavy tails and input rates.
Some References:
[1] D'Auria, B., Resnick, S.I. (2006) Data network models of burstiness. Adv. in Appl. Probab., 38(2):373{404.
[2] D'Auria, B., Resnick, S.I. (2007) The influence of dependence on data network models. Technical report, Cornell University, 2006b. Report #1449, Available at legacy.orie.cornell.edu/ sid.
[3] Mikosch, T., Resnick, S.I. (2006) Activity rates with very heavy tails. Stochastic Process. Appl., 116:131{155.
[4] Mikosch, T., Resnick, S.I., Rootzen, H. and Stegeman, A.W. (2002) Is network traffic approximated by stable Levy motion or fractional Brownian motion? Ann. Appl. Probab., 12(1):23-68.
[5] Resnick, S.I. (2006) Heavy Tail Phenomena: Probabilistic and Statistical Modeling. Springer Series in Operations Research and Financial Engineering.
Springer-Verlag, New York, ISBN: 0-387-24272-4.
[6] Resnick, S.I.,Rootzen, H. (2000) Self-similar communication models and very heavy tails. Ann. Appl. Probab., 10:753{778.
