Overflow probability in an ATM queue with self-similar input traffic
- 22 November 2002
- proceedings article
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 2, 822-826
- https://doi.org/10.1109/icc.1997.609995
Abstract
Real measurements in high-speed communications networks have recently shown that traffic may demonstrate properties of long-range dependency peculiar to self-similar stochastic processes. Measurements have also shown that, with increasing buffer capacity, the resulting cell loss is not reduced exponentially fast as it is predicted by Markov-model-based queueing theory but, in contrast, decreases very slowly. Presenting a theoretical understanding to those experimental results is still a problem. The paper presents mathematical models for self-similar cell traffic and analyzes the overflow behavior of a finite-size ATM buffer fed by such a traffic. An asymptotical upper bound to the overflow probability, which decreases hyperbollically, h a - , with buffer-size- h is obtained. A lower bound is also described, which demonstrates the same h a - asymptotical behavior, thus showing an actual hyperbolical decay of overflow probability for a self-similar-traffic model.Keywords
This publication has 7 references indexed in Scilit:
- Analysis of an ATM buffer with self-similar ("fractal") input trafficPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Self-similar traffic and upper bounds to buffer-overflow probability in an ATM queuePerformance Evaluation, 1998
- On self-similar traffic in ATM queues: definitions, overflow probability bound, and cell delay distributionIEEE/ACM Transactions on Networking, 1997
- On the self-similar nature of Ethernet traffic (extended version)IEEE/ACM Transactions on Networking, 1994
- Regular VariationPublished by Cambridge University Press (CUP) ,1987
- Functions of probability measuresJournal d'Analyse Mathématique, 1973
- A Theorem on Sums of Independent Positive Random Variables and Its Applications to Branching Random ProcessesTheory of Probability and Its Applications, 1964