Traditionally the Internet is used for the following applications: FTP, e-mail and Web\ud traffic. However in the recent years the Internet is increasingly supporting emerging\ud applications such as IP telephony, video conferencing and online games. These new\ud applications have different requirements in terms of throughput and delay than\ud traditional applications. For example, interactive multimedia applications, unlike\ud traditional applications, have more strict delay constraints and less strict loss constraints.\ud Unfortunately, the current Internet offers only a best-effort service to all applications\ud without any consideration to the applications specific requirements.\ud In this thesis three existing Active Queue Management (AQM) mechanisms are\ud modified by incorporating into these a control function to condition routers for better\ud Quality of Service (QoS). Specifically, delay is considered as the key QoS metric as it is\ud the most important metric for real-time multimedia applications. The first modified\ud mechanism is Drop Tail (DT), which is a simple mechanism in comparison with most\ud AQM schemes. A dynamic threshold has been added to DT in order to maintain packet\ud queueing delay at a specified value. The modified mechanism is referred to as Adaptive\ud Drop Tail (ADT). The second mechanism considered is Early Random Drop (ERD) and,\ud iii\ud in a similar way to ADT, a dynamic threshold has been used to keep the delay at a\ud required value, the main difference being that packets are now dropped probabilistically\ud before the queue reaches full capacity. This mechanism is referred to as Adaptive Early\ud Random Drop (AERD). The final mechanism considered is motivated by the well\ud known Random Early Detection AQM mechanism and is effectively a multi-threshold\ud version of AERD in which packets are dropped with a linear function between the two\ud thresholds and the second threshold is moveable in order to change the slope of the\ud dropping function. This mechanism is called Multi Threshold Adaptive Early Random\ud Drop (MTAERD) and is used in a similar way to the other mechanisms to maintain\ud delay around a specified level.\ud The main focus with all the mechanisms is on queueing delay, which is a significant\ud component of end-to-end delay, and also on reducing the jitter (delay variation) A\ud control algorithm is developed using an analytical model that specifies the delay as a\ud function of the queue threshold position and this function has been used in a simulation\ud to adjust the threshold to an effective value to maintain the delay around a specified\ud value as the packet arrival rate changes over time.\ud iv\ud A two state Markov Modulated Poisson Process is used as the arrival process to each of\ud the three systems to introduce burstiness and correlation of the packet inter-arrival times\ud and to present sudden changes in the arrival process as might be encountered when TCP\ud is used as the transport protocol and step changes the size of its congestion window.\ud In the investigations it is assumed the traffic source is a mixture of TCP and UDP traffic\ud and that the mechanisms conserved apply to the TCP based data. It is also assumed that\ud this consists of the majority proportion of the total traffic so that the control\ud mechanisms have a significant effect on controlling the overall delay.\ud The three mechanisms are evaluated using a Java framework and results are presented\ud showing the amount of improvement in QoS that can be achieved by the mechanisms\ud over their non-adaptive counterparts. The mechanisms are also compared with each\ud other and conclusions drawn
To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.