Multicast routing with quality of service and traffic engineering requirements in the Internet, based on genetic algorithm

P.T. de Araujo, G.M.B. de Oliveira
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引用次数: 10

Abstract

In order to deal with the high computational power required by the QoS routing, the use of genetic algorithm (GA) as a method to obtain the appropriate routes has been presented in various works. The GA discussed in this work was adapted from the model presented by Erdun et al. (2001), that uses bandwidth, delay and cost as metrics to evaluate the routes. Two innovations were incorporated in the GA in order to attend traffic engineering requirements: inclusion of the metric number of steps (or hops) in the route evaluation, and a mechanism to avoid the generation of repeated individuals producing several optimal and sub-optimal routes. In order to test the proposed genetic algorithm, two examples of network topology were used. The results indicate that the GA discussed in this work converges to the global optimal solution, while the implementations discussed in Ravikumar et al. (1998) and Erdun et al. did not reach it. Besides, even in the runs that the GA did not converge to the global optimum, sub-optimal solutions that attend to the constraint delay were obtained with a small increment in the cost.
基于遗传算法的多播路由是Internet中满足业务质量和流量工程要求的一种路由算法
为了解决QoS路由对计算能力要求较高的问题,各种工作都提出了使用遗传算法(GA)作为获得合适路由的方法。本工作中讨论的遗传算法改编自Erdun等人(2001)提出的模型,该模型使用带宽、延迟和成本作为评估路由的指标。为了满足交通工程的要求,在遗传算法中加入了两个创新:在路线评估中包含度量步数(或跳数),以及避免产生重复的个体产生若干最优和次最优路线的机制。为了验证所提出的遗传算法,使用了两个网络拓扑实例。结果表明,本文讨论的遗传算法收敛于全局最优解,而Ravikumar等人(1998)和Erdun等人讨论的实现并没有达到全局最优解。此外,即使在遗传算法没有收敛到全局最优的运行中,考虑到约束延迟的次最优解也会以较小的代价增加。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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