An average queue weight parameterization in a network supporting TCP flows with RED

In this paper we use a previously developed RED (random early detection) model to analyze and develop a quantitative approach of defining one of the RED parameters, average queue weight, while the network load level varies. First, we introduce the linear control system and the pre-developed RED model. Based on this model, we next develop a proposition aiming at the RED parameter, average queue weight, and the load level only, to ensure the system stay stable. Our research is intended to provide a quantitative basis for parameterizing RED while load level varies. We present ns simulations to support our analysis

An Average Queue Weight Parameterization in a Network Supporting TCP Flows with RED

In this paper we use a previously developed RED (random early detection) model to analyze and develop a quantitative approach of defining one of the RED parameters, average queue weight, while the network load level varies. First, we introduce the linear control system and the pre-developed RED model. Based on this model, we next develop a proposition aiming at the RED parameter, average queue weight, and the load level only, to ensure the system stay stable. Our research is intended to provide a quantitative basis for parameterizing RED while load level varies. We present ns simulations to support our analysis

Parameter self-tuning in internet congestion control

Active Queue Management (AQM) aims to achieve high link utilization, low queuing delay and low loss rate in routers. However, it is difficult to adapt AQM parameters to constantly provide desirable transient and steady-state performance under highly dynamic network scenarios. They need to be a trade-off made between queuing delay and utilization. The queue size would become unstable when round-trip time or link capacity increases, or would be unnecessarily large when round-trip time or link capacity decreases. Effective ways of adapting AQM parameters to obtain good performance have remained a critical unsolved problem during the last fifteen years. This thesis firstly investigates existing AQM algorithms and their performance. Based on a previously developed dynamic model of TCP behaviour and a linear feedback model of TCP/RED, Auto-Parameterization RED (AP-RED) is proposed which unveils the mechanism of adapting RED parameters according to measurable network conditions. Another algorithm of Statistical Tuning RED (ST-RED) is developed for systematically tuning four key RED parameters to control the local stability in response to the detected change in the variance of the queue size. Under variable network scenarios like round-trip time, link capacity and traffic load, no manual parameter configuration is needed. The proposed ST-RED can adjust corresponding parameters rapidly to maintain stable performance and keep queuing delay as low as possible. Thus the sensitivity of RED's performance to different network scenarios is removed. This Statistical Tuning algorithm can be applied to a PI controller for AQM and a Statistical Tuning PI (ST-PI) controller is also developed. The implementation of ST-RED and ST-PI is relatively straightforward. Simulation results demonstrate the feasibility of ST-RED and ST-PI and their capabilities to provide desirable transient and steady-state performance under extensively varying network conditions.EThOS - Electronic Theses Online ServiceGBUnited Kingdo