Direct Research Journal of Engineering and Information Technology Vol.1 (3), pp. 54-63,September 2013 Available online at http://directresearchpublisher.org/drjet © 2013 Direct Research Journals Publisher Original Research Paper Dropping policies for transient network traffic congestion control Abah Joshua 1 *,Alibaba Dauda 2 and Abah O. Sunday 3 1 Department of Computer Engineering, Faculty of Engineering, University of Maiduguri,Nigeria. 2 Computer and Internet Centre, University of Maiduguri,Nigeria. 3 Field Engineer, Coollink Lagos, Nigeria. ABSTRACT This paper examines the effects of dropping policies on dropping probabilities for multi-class traffic and compares their performance and support for Quality of Service (QoS). The flexibility of these dropping policies is illustrated with respect to performance parameters and traffic characteristics. The method used for this evaluation is simulation on a model network that carries traffic of different class; real- time and bursty (non real-time) traffic. Results showed that Weighted Random Early Detection (WRED) in conjunction with an appropriate queuing discipline provides better support for Quality of Service of real time applications while Tail Drop supports best effort applications. Key words: Tail Drop, Random Early Detection, Simulation, Quality of Service, Queuing *Corresponding Author E-mail: jehoshua_a@yahoo.com, abah@unimaid.edu.ng Accepted 22 August, 2013 INTRODUCTION With rapid growth of the internet, the amount of traffic (real-time and non real-time) such as voice over IP (VoIP) calls, multimedia and even non real-time applications like file transfer protocol (FTP), e-mails etc have been increased. The requirements of real-time traffic in the internet are; i. Low loss rate and ii. Low end-to-end delay Routers in the internet have to reflect these requirements to support real-time traffic. Active Queue Management (AQM) such as Tail Drop (DT), Random Early Detection (RED) and Weighted Random Early Detection (WRED) are usually used to control congestion in a router and Quality of Service (QoS) in the entire network (Chaudhuri et al, 2006). Fortunately, because end-to-end delay is primarily determined by the buffering, queuing and routing delay of routers, the same resources are related to end-to-end delay and congestion control. This means that active queue management can be used to both control congestion and support different classes and types of applications’ traffic in the internet. Among active management schemes in which dropping policies are part, this paper seek to compare these schemes to see which of them has a good feature to bound the queuing delay and loss rate desired. MOTIVATION The digital divide separates the information rich and the information poor. The Organization for Economic Co- operation and Development defines the digital divide as the difference between individuals, households, businesses and geographic areas with regard to (a) their opportunities to access ICTs and (b) their use of the Internet for a wide variety of activities (Emmanuel, 2003). It is the gap between those who have real access to information and communications technology and who are able to use it effectively, and those who don’t have such access. The digital divide is usually measured in terms of citizen/population access to ICT. Among the indicators for measuring access are telephone density (teledensity), personal computer (PC) deployment and penetration and