PERFORMANCE MODELLING OF TRAFFIC CONGESTION IN WIRELESS NETWORKS

2006 ◽  
Vol 07 (01) ◽  
pp. 163-177
Author(s):  
ASFAND-E YAR ◽  
I. U. AWAN ◽  
M. E. WOODWARD

Evolution in Wireless Technologies and Networks imposes a greater need for network support as current congestion control and avoidance techniques are mainly designed for wired networks. The current performance evaluation techniques proposed for wireless networks are not able to achieve optimal performance to guarantee desired Quality of Service (QoS) standards. Thus, the new schemes such as Active Queue Management (AQM) are needed to be adaptive to dynamic wireless networks and bursty traffic conditions to help in avoiding severe performance degradation in wireless environment. Thus, in this paper we developed and validated a novel approximate analytical performance model of a multiple threshold Random Early Detection (RED) congestion control mechanism based on the principle of Maximum Entropy (ME). It can be employed at the wireless gateways/base stations to regulate the buffer management and bandwidth allocation. Closed form expressions for the state and blocking probabilities have also been characterized. Numerical examples have been presented for aggregate and marginal QoS measures, which show the credibility of the ME solution and its validation against simulation.

2007 ◽  
Vol 08 (04) ◽  
pp. 369-385
Author(s):  
LAN WANG ◽  
GEYONG MIN ◽  
IRFAN AWAN

Traffic congestion degrades not only the user-perceived Quality-of-Service (QoS), such as leading to high packet loss rates, low throughput, and increased delays, but also causes excessive energy consumption in energy-sensitive systems (e.g., wireless sensor networks). A simple way to detect congestion is to monitor and measure queue length in network nodes or routers. This paper develops an analytical performance model for a finite capacity queueing system with an enhanced Random Early Detection (RED) congestion control scheme based on the instantaneous queue length in the presence of differentiated classes of bursty traffic. The aggregate traffic is captured by the superposition of 2-state Markov Modulated Poisson Processes (MMPP). The individual threshold is assigned to each traffic class in order to differentially control traffic injection rate. The accuracy of this model is verified by comparing the analytical results against those obtained from simulation experiments. The model is adopted to investigate the effects of traffic burstiness and system capacity on the performance of the congestion control scheme.


2007 ◽  
Vol 115 (3) ◽  
pp. 416-424 ◽  
Author(s):  
Peter A. Valberg ◽  
T. Emilie van Deventer ◽  
Michael H. Repacholi

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