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QoS multicast routing for multimedia group communications using intelligent computational methods

机译:使用智能计算方法的多媒体组通信的QoS多播路由

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摘要

In this paper, we investigate the problem of quality-of-service (QoS) multicast routing for multimedia group communications. We first develop a unified framework for achieving QoS multicast trees using intelligent computational methods. The framework consists of the model for multimedia communication network, the formulation of QoS multicast routing problem, and three key components used in intelligent computational methods-based QoS multicast routing algorithms. Then we propose three QoS multicast algorithms based on three representative intelligent computational methods (i.e., genetic algorithm, simulated annealing, and Tabu search), separately. In these algorithms, both the network resource requirements and the end-to-end delay are considered as the QoS parameters. Various issues are analyzed and designed for applying these three intelligent computational methods to construct QoS multicast trees. By simulation, we evaluate the performance of these three algorithms on a small-scale real-world multimedia communication network and a randomly generated large-scale network. Simulation results show that our algorithms can find near-optimal QoS multicast trees with high success rate. We also compare the running time among them, which help explain the algorithmic structures.
机译:在本文中,我们研究了用于多媒体组通信的服务质量(QoS)多播路由问题。我们首先开发一个统一的框架,以使用智能计算方法来实现QoS多播树。该框架包括多媒体通信网络模型,QoS多播路由问题的表述以及基于智能计算方法的QoS多播路由算法中使用的三个关键组件。然后,我们分别基于三种代表性的智能计算方法(即遗传算法,模拟退火和禁忌搜索)提出了三种QoS组播算法。在这些算法中,网络资源需求和端到端延迟都被视为QoS参数。分析和设计了各种问题,以应用这三种智能计算方法来构建QoS多播树。通过仿真,我们评估了这三种算法在小型现实世界的多媒体通信网络和随机生成的大型网络上的性能。仿真结果表明,我们的算法可以找到成功率较高的接近最优的QoS多播树。我们还比较了它们之间的运行时间,这有助于解释算法结构。

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