Simulation and Comparison of Csfq, Red & Fred Queuing Techniques

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  • Topic: Scheduling algorithm, Active Queue Management, Fair queuing
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International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-2, Issue-3, July 2012

Simulation & Comparison of CSFQ, RED & FRED Queuing Techniques Sandeep, Rambir Joon, Manveen Singh Chadha
ABSTRACT-Today’s Internet only provides Best Effort Service. Traffic is processed as quickly as possible, but there is no guarantee of timelines or actual delivery. With the rapid transformation of the Internet into a commercial infrastructure, demands for service quality have rapidly developed. People of the modern world are very much dependent on various network services like VOIP, Videoconferencing and File Transfer. Different types of Traffic Management systems are used in those services. Queuing is one of the very vital mechanisms in traffic management system. Each router in the network must implement some queuing discipline that governs how packets are buffered while waiting to be transmitted. This paper gives a comparative analysis of three queuing systems CSFQ, RED and FRED. The study has been carried out on some issues like: Throughput, packet end to end delay and packet delay fraction rate the simulation results shows that CSFQ technique has a superior quality than the oth techniques. Keywords: RED (Random Early Drop), FRED (Flow random Early Drop) and CSFQ (Core Stateless fair Queuing)

II.

PACKET HANDLING TECHNIQUES

Various queuing disciplines can be used to control which packets get transmitted and which packets which packets get dropped. The queuing disciplines are: 1. 2. 3. A. RED (Random Early Drop) FRED (Flow random Early Drop) CSFQ (Core Stateless fair Queuing)

I.

INTRODUCTION

Today‟s Internet only provides best effort service and traffic is processed as quickly as possible, but there is no guarantee for the timely delivery of data. So, the ability to provide flow based quality of service (QoS) support has become very important for the design of modern switches and routers. With the development of the Internet network in recent years, a variety of novel Internet multimedia applications, such as voice over IP and videoconferencing, have been developed, which usually have different quality of service requirements. In order to complete various processes successfully, the network should maintain a good QoS (Quality of Service) to provide satisfactory Results to the user. QoS must be efficient to differentiate the traffic and satisfy their specific requirements. As the traffic on network is increasing due to congestion, it decades the performance of network, the different priorities can be assigned to different applications to enhance the performance of network. This paper demonstrates the performance of a number of packet handling mechanisms and produces a comparative picture of them using the simulation software NS-2(Network Simulator-2).

Red (random early drop) The basic idea behind RED queue management is to detect incipient congestion early and to convey congestion notification to the end-hosts, allowing them to reduce their transmission rates before queues in the network overflow and packets are dropped. To do this, RED maintains an exponentiallyweighted moving average (EWMA) of the queue length which it uses to detect congestion. When the average queue length exceeds a minimum threshold (minth), packets are randomly dropped or marked with an explicit congestion notification (ECN) bit. When the average queue length exceeds a maximum threshold (maxth), all packets are dropped or marked. Random Early Detection (RED) keeps no per flow state information. Packets are dropped probabilistically based on the long-term average queue size and fixed indicators of congestion (thresholds). RED uses randomization to drop arriving packets to avoid biases against bursty traffic and roughly drops packets in proportion to the flows data rate at the router. However, flows with high RTTs and small window sizes are bursty, and this burstiness causes high variability in the perceived data...
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