Design and Performance Analysis of Link-by-Link Congestion Avoidance (LbLCA) Algorithm

IF 1 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS
Sneha K. Thombre, L. Patnaik, A. S. Tavildar
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引用次数: 0

Abstract

In this paper, a simple but new approach, namely link-by-link congestion avoidance (LbLCA) algorithm, which works at the network layer, has been proposed. LbLCA is a proactive congestion avoidance algorithm. It uses explicit feedback to prevent congestion to happen in the first place. The novelty of LbLCA is that no per flow information is required, which makes it more scalable. Based on the design philosophy and equations, sizing of various router buffers has been arrived at for different typical network topologies. Buffer sizes depend upon the mean arrival rate at router input and outgoing link capacities and are independent of round trip time (RTT) and the number of flows passing through the router. The buffer sizes determined using LbLCA design are validated using extensive NS2 simulations. The performance evaluation has been done using NS2 simulations on the typical network topologies. The performance comparison between TCP and LbLCA reveals that proposed LbLCA algorithm gives improved performance for the end-to-end delay and packet delivery ratio. LbLCA is impartial to all flows, as LbLCA works at network layer and therefore, cannot differentiate between flows.
链路拥塞避免(LbLCA)算法的设计与性能分析
本文提出了一种简单但新的方法,即在网络层工作的逐链路拥塞避免(LbLCA)算法。LbLCA是一种主动式拥塞避免算法。它首先使用明确的反馈来防止拥塞的发生。LbLCA的新颖之处在于不需要每个流的信息,这使它更具可扩展性。基于设计原理和方程,已经针对不同的典型网络拓扑确定了各种路由器缓冲区的大小。缓冲区大小取决于路由器输入和输出链路容量的平均到达率,并且与往返时间(RTT)和通过路由器的流的数量无关。通过广泛的NS2模拟验证了使用LbLCA设计确定的缓冲区大小。在典型的网络拓扑结构上使用NS2仿真进行了性能评估。TCP和LbLCA算法的性能比较表明,该算法在端到端延迟和数据包传输率方面都有所提高。LbLCA对所有流都是公正的,因为它在网络层工作,因此无法区分流。
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来源期刊
CiteScore
2.50
自引率
46.20%
发文量
57
期刊介绍: IJCNDS aims to improve the state-of-the-art of worldwide research in communication networks and distributed systems and to address the various methodologies, tools, techniques, algorithms and results. It is not limited to networking issues in telecommunications; network problems in other application domains such as biological networks, social networks, and chemical networks will also be considered. This feature helps in promoting interdisciplinary research in these areas.
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