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Optimizing co-existing multicast routing trees in IP network via discrete artificial fish school algorithm

机译:通过离散人工鱼群算法优化IP网络中共存的组播路由树

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Increasingly more internet-applications require the supports of multiple multicasts, while existing multicast routing algorithms only aim to establish a single multicast routing tree (MRT). Sequentially optimizing the co-existing MRTs each is a common practice of handling the situation of multiple multicast session being concurrent, but it results in link-congestion on low-cost routes, or leads to non-optimal solution even if bandwidth reservation strategy is adopted. This paper proposes to optimize multiple co-existing MRTs as a whole via one-off optimization instead of sequentially optimizing each in isolation. To carry out the one-off optimization, a discrete artificial fish school algorithm (DAFSA) is proposed. The simulation results show that the proposed DAFSA is capable of optimally packing co-existing MRTs and exhibits remarkably better ability than several the most representative state-of-the-art algorithms in the sense of avoiding the link-congestion and minimizing the overall tree cost. The running time of the proposed DAFSA fully meets the requirements of the practical IP multicasting. Besides, Monte Carlo test proves that the convergence of the proposed DAFSA is not sensitive to its parameters. (C) 2019 Elsevier B.V. All rights reserved.
机译:越来越多的Internet应用程序需要多个组播的支持,而现有的组播路由算法仅旨在建立单个组播路由树(MRT)。对每个共存的MRT进行顺序优化是处理多个多播会话并发的一种常见做法,但是这会导致低成本路由上的链路拥塞,或者即使采用带宽预留策略,也会导致非最佳解决方案。本文提出通过一次性优化从整体上优化多个共存的MRT,而不是孤立地依次优化每个MRT。为了进行一次性优化,提出了一种离散人工鱼群算法(DAFSA)。仿真结果表明,在避免链路拥塞和最大程度降低总树成本的意义上,提出的DAFSA能够最佳地打包并存的MRT,并且比几种最有代表性的最新算法表现出明显更好的能力。 。提出的DAFSA的运行时间完全满足实际IP多播的要求。此外,蒙特卡洛测试证明了提出的DAFSA的收敛性对其参数不敏感。 (C)2019 Elsevier B.V.保留所有权利。

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