Minimal-delay traffic grooming for WDM star networks

Hongsik Choi, Nikhil Garg, Hyeong-Ah Choi
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引用次数: 2

Abstract

All-optical networks face the challenge of reducing slower opto-electronic conversions by managing assignment of traffic streams to wavelengths in an intelligent manner, while at the same time utilizing bandwidth resources to the maximum. This challenge becomes harder in networks closer to the end users that have insufficient data to saturate single wavelengths as well as traffic streams outnumbering the usable wavelengths, resulting in traffic grooming which requires costly traffic analysis at access nodes. We study the problem of traffic grooming that reduces the need to analyze traffic, for a class of network architecture most used by Metropolitan Area Networks; the star network. The problem being NP-complete, we provide an efficient twice-optimal-bound greedy heuristic for the same, that can be used to intelligently groom traffic at the LANs to reduce latency at the access nodes. Simulation results show that our greedy heuristic achieves a near-optimal solution.
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WDM星型网络的最小延迟流量疏导
全光网络面临的挑战是以智能的方式管理流量流的波长分配,同时最大限度地利用带宽资源,从而减少较慢的光电转换。在靠近最终用户的网络中,这一挑战变得更加困难,因为这些网络没有足够的数据来饱和单个波长以及流量流超过可用波长,从而导致流量疏导,这需要在接入节点上进行昂贵的流量分析。针对城域网最常用的一类网络架构,我们研究了减少流量分析需求的流量疏导问题;明星网络。该问题是np完全的,我们提供了一个有效的两次最优边界贪婪启发式算法,可用于智能地训练局域网中的流量,以减少访问节点的延迟。仿真结果表明,我们的贪心启发式算法得到了近似最优解。
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