Xuefen Chi

QoS Enhancements and Performance Analysis for Delay Sensitive Applications

This paper presents a comprehensive system modeling and analysis approach for both predicting and controlling queuing delay at an expected value under multi-class traffic in a single buffer. This approach could effectively enhance QoS delivery for delay sensitive applications. Six major contributions are given in the paper: (1) a discrete-time analytical model is developed for capturing multi-class traffic with binomial distribution; (2) a control strategy with dynamic queue thresholds is used in simulation experiments to control the delay at a specified value within the buffer; (3) the feasibility of the system is validated by comparing theoretical analysis with simulation scenarios; (4) the arrival rate can be adjusted for each forthcoming time window during the simulation with multi-packet sources; (5) statistical evaluation is performed to show both efficiency and accuracy of the analytical and simulation results; (6) a graphical user interface is developed that can provide flexible configuration for the simulation and validate input values.

Delay Restraining of Combined Multiple Input Cross Core Router

Delay is arguably one of the most vital Quality of Service (QoS) metrics in computer networks since it has many implications for real time multimedia applications. The congestion and queuing delay of Internet core routers is one of the most important fundamental research topics which plays a vital role in determination of the overall end-to-end delay. In previous works, the control algorithm proposed and modelled in the discrete-time domain, by controlling the value of the threshold within the queue buffer to achieve the required delay, is based on a single arrival in one time slot. In this paper, a new traffic model with the ability to capture the multiple arrivals in a single time slot is described and its application for restraining the delay to a pre-defined value within the network core router is developed. The feasibility of the system is examined using both theoretical analysis and simulation. A statistical evaluation is performed to show both efficiency and accuracy of the results acquired throughout the simulation.

Modeling of Network Performance for Large-Scale System with Congestion Control Mechanism

Communication and computer networks are usually considered to be large-scale systems which are difficult to analyze and evaluate the performance since they are extremely complex in density and scale. Congestion control mechanism for such large-scale system is very important and is also what we care about in this paper. We investigate and develop a novel cost effective analytical model to provide a suitable system performance modeling solution for large-scale systems with Random Early Detection (RED) mechanism. The large-scale system is abstracted as a discrete-time tandem queuing network with finite capacity where each node is implemented with RED algorithm. Since the exact closed-form solution is difficult to get, an approximate method which is composed of a decomposition technique and a parameter fitting algorithm is used in this paper. The credibility of the analytical model is verified by simulation. Both the single-node performance metrics and end-to-end performance measures are included to demonstrate how RED algorithm can affect the performance of the large-scale system.