This paper presents Random Early Detection (RED) gate-ways for congestion avoidance in packet-switched networks. The gateway detects incipient congestion by com-puting the average queue size. The gateway could notify connections of congestion either by dropping packets ar-riving at the gateway or by setting a bit in packet headers. When the average queue size exceeds a preset threshold,the gateway drops or marks each arriving packet with a certain probability, where the exact probability is a func-tion of the average queue size. RED gateways keep the average queue size low while allowing occasional bursts of packets in the queue. During congestion, the probability that the gateway notifies a particular connection to reduce its window is roughly proportional to that connection's share of the bandwidth throughthe gateway. RED gateways are designed to accompany a transport-layer congestion control protocol such as TCP.The RED gateway has no bias against bursty traffic and avoids the global synchronization of many connectionsdecreasing their window at the same time. Simulations of a TCP/IP network are used to illustrate the performance of RED gateways.

In this paper we explore the bias in TCP/IP networks against connections with multiple congested gateways. We consider the interaction between the bias against connections with multiple congested gateways, the bias of the TCP window modification algorithm against connections with longer roundtrip times, and the bias of Drop Tail and Random Drop gateways against bursty traffic. Using simulations and a heuristic analysis, we show that in a network with the window modification algorithm in 4.3 tahoe BSD TCP and with Random Drop or Drop Tail gateways, a longer connection with multiple congested gateways can receive unacceptably low throughput. We show that in a network with no bias against connections with longer roundtrip times and with no bias against bursty traffic, a connection with multiple congested gateways can receive an acceptable level of throughput. We discuss the application of several current measures of fairness to networks with multiple congested gateways, and show that diff...

Effective large-scale network simulation is a difficult task to accomplish efficiently due to complex and dynamic routing tables, route stability, along with many other complexities. Due to the collective power of the Internet routers used in traffic routing, the Internet comprises the world’s largest distributed computer system, which in turn makes network simulation increasingly difficult. Despite these complexities, network simulation is needed as a research tool to further understand large networks (such as the Internet), routing capabilities, and new technologies. Network simulation is not only necessary to further our understanding of large and small networks, but can potentially be applied to other areas of science, including biotechnology, electrical engineering, and virtually every other facet of science and high-technology fields. The aim of this paper is to outline current network simulation tools and techniques, and to describe a recently devel-oped tool used to convert domain modeling language files between two simulators, SSFNet and Genesis.