Hostname: page-component-586b7cd67f-2brh9 Total loading time: 0 Render date: 2024-11-25T06:00:07.378Z Has data issue: false hasContentIssue false

PERFORMANCE ANALYSIS OF THE RANDOM EARLY DETECTION ALGORITHM

Published online by Cambridge University Press:  22 May 2002

V. Sharma
Affiliation:
Department of Electrical Communication Engineering, Indian Institute of Science, Bangalore, 560012, India, E-mail: [email protected]
J. Virtamo
Affiliation:
Networking Laboratory, Helsinki University of Technology, FIN-02015 HUT, Finland, E-mail: Jorma.Virtamo
P. Lassila
Affiliation:
Networking Laboratory, Helsinki University of Technology, FIN-02015 HUT, Finland, E-mail: [email protected]

Abstract

In this article we consider a finite queue with its arrivals controlled by the random early detection algorithm. This is one of the most prominent congestion avoidance schemes in the Internet routers. The aggregate arrival stream from the population of transmission control protocol sources is locally considered stationary renewal or Markov modulated Poisson process with general packet length distribution. We study the exact dynamics of this queue and provide the stability and the rates of convergence to the stationary distribution and obtain the packet loss probability and the waiting time distribution. Then we extend these results to a two traffic class case with each arrival stream renewal. However, computing the performance indices for this system becomes computationally prohibitive. Thus, in the latter half of the article, we approximate the dynamics of the average queue length process asymptotically via an ordinary differential equation. We estimate the error term via a diffusion approximation. We use these results to obtain approximate transient and stationary performance of the system. Finally, we provide some computational examples to show the accuracy of these approximations.

Type
Research Article
Copyright
© 2002 Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)