基于随机下一跳策略的全光网络中跟踪分配监控

Yangming Zhao, Shizhong Xu, Bin Wu, Xiong Wang, Sheng Wang
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引用次数: 5

摘要

监测轨迹(m-trail)的概念为全光网络中快速、明确的链路故障定位提供了一种引人注目的机制。为了在大型网络中实现快速的m-trail设计,针对最优的整数线性规划(ILP)模型,提出了两种高效的启发式算法RCA+RCS和MTA。然而,RCA+RCS存在不相交轨迹问题,这增加了所需的m-trail数量,而MTA总是找到一个确定性的解决方案,但由于解空间有限,可能不够好。在本文中,我们提出了一种新的启发式RNH-MTA(随机下一跳监控路径分配策略)来解决这些问题。与MTA类似,RNH-MTA保证了每个m-trail的有效光学结构,并依次向解中添加必要的m-trail,从而避免了不相交的trail问题。RNH-MTA通过用随机下一跳策略代替MTA中的确定性搜索,建立了扩展每条m-trail的概率模型。这不仅扩大了解空间,增加了解的多样性,而且在解的质量和算法的运行时间之间进行了可控的权衡。我们的数值结果表明RNH-MTA比RCA+RCS和MTA都有优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Monitoring Trail Allocation in all-optical networks with the Random Next Hop Policy
The concept of monitoring trail (m-trail) provides a striking mechanism for fast and unambiguous link failure localization in all-optical networks. To achieve fast m-trail design in large-size networks, two efficient heuristics RCA+RCS and MTA are proposed against the optimal ILP (Integer Linear Program) model. However, RCA+RCS suffers from the disjoint trail problem which increases the required number of m-trails, and MTA always finds a deterministic solution which may not be good enough due to the limited solution space. In this paper, we propose a new heuristic RNH-MTA (Monitoring Trail Allocation with the Random Next Hop policy) to solve those issues. Similar to MTA, RNH-MTA ensures a valid optical structure of each m-trail and sequentially adds necessary m-trails to the solution, and thus is free of the disjoint trail problem. By replacing the deterministic searching in MTA using the Random Next Hop policy, RNH-MTA sets up a probabilistic model in extending each m-trail. This not only enlarges the solution space and increases the solution diversity, but also enables a controllable tradeoff between the solution quality and the running time of the algorithm. Our numerical results show the advantages of RNH-MTA over both RCA+RCS and MTA.
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