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首页> 外文期刊>IEEE Journal on Selected Areas in Communications >A Lagrangean relaxation-based approach for routing and wavelength assignment in multigranularity optical WDM networks
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A Lagrangean relaxation-based approach for routing and wavelength assignment in multigranularity optical WDM networks

机译:基于拉格朗日松弛法的多粒度光WDM网络中的路由和波长分配方法

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摘要

Optical wavelength-division multiplexed (WDM) networks often include optical cross-connects with multigranularity switching capability, such as switching on a single lambda, a waveband, or an entire fiber basis. In addition, it has been shown that routing and wavelength assignment (RWA) in an arbitrary mesh WDM network is an NP-complete problem. In this paper, we propose an efficient approximation approach, called Lagrangean relaxation with heuristics (LRH), aimed to resolve RWA in multigranularity WDM networks particularly with lambda and fiber switches. The task is first formulated as a combinatorial optimization problem in which the bottleneck link utilization is to be minimized. The LRH approach performs constraint relaxation and derives a lower-bound solution index according to a set of Lagrangean multipliers generated through subgradient-based iterations. In parallel, using the generated Lagrangean multipliers, the LRH approach employs a new heuristic algorithm to arrive at a near-optimal upper-bound solution. With lower and upper bounds, we conduct a performance study on LRH with respect to accuracy and convergence speed under different parameter settings. We further draw comparisons between LRH and an existing practical approach via experiments over randomly generated and several well-known large sized networks. Numerical results demonstrate that LRH outperforms the existing approach in both accuracy and computational time complexity, particularly for larger sized networks.
机译:光波分复用(WDM)网络通常包括具有多粒度交换功能的光交叉连接,例如基于单个λ,波段或整个光纤的交换。另外,已经表明,任意网格WDM网络中的路由和波长分配(RWA)是NP完全问题。在本文中,我们提出了一种有效的近似方法,称为启发式拉格朗日松弛法(LRH),旨在解决多粒度WDM网络中的RWA,尤其是使用lambda和光纤交换机的情况。首先将任务表述为组合优化问题,其中要最大程度地减少瓶颈链路的利用率。 LRH方法执行约束松弛,并根据通过基于次梯度的迭代生成的一组拉格朗日乘数来得出下界解索引。并行地,LRH方法使用生成的拉格朗日乘数,采用新的启发式算法来获得接近最佳的上限解。通过上下限,我们在不同参数设置下针对精度和收敛速度对LRH进行了性能研究。通过对随机生成的和几个众所周知的大型网络进行的实验,我们进一步对LRH和现有的实用方法进行了比较。数值结果表明,LRH在准确性和计算时间复杂度方面均优于现有方法,特别是对于大型网络。

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