A Novel Approach for Failure Localization in All-Optical Mesh Networks
A Novel Approach for Failure Localization in All-Optical Mesh Networks
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DOI：
10.1109/tnet.2010.2068057
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发表时间：
2011-02
期刊：
影响因子：
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通讯作者：
János Tapolcai;Bin Wu;P. Ho;L. Rónyai
中科院分区：
文献类型：
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作者：
János Tapolcai;Bin Wu;P. Ho;L. Rónyai
实现快速和精确的故障定位一直是全光网状网络中高度期望的特征。监控路径（m-trail）已被提出作为最通用的监控结构，用于实现任何单个链路故障的明确故障定位（UFL），同时有效地减少淹没网络的警报信号的量。然而，关键是要拿出一个快速和智能的m-trail的设计方法，以尽量减少m-trail的数量和总带宽消耗，无处不在的决定了长度的报警代码和带宽开销的m-trail部署，分别。本文研究了m-轨迹设计问题。为了更深入地理解这个问题，我们首先对UFL在最稀疏（即，环）和致密（即，全网状）拓扑。在此基础上，提出了一种基于随机码分配（RCA）和随机码交换（RCS）的多路径设计算法。通过与整数线性规划（ILP）方法的比较，验证了该算法在最小化故障管理成本和带宽消耗的同时显著减少计算时间方面的优越性。为了研究拓扑多样性的影响，大量的模拟进行了数千个随机的网络拓扑结构，系统地增加网络密度。
Achieving fast and precise failure localization has long been a highly desired feature in all-optical mesh networks. Monitoring trail (m-trail) has been proposed as the most general monitoring structure for achieving unambiguous failure localization (UFL) of any single link failure while effectively reducing the amount of alarm signals flooding the networks. However, it is critical to come up with a fast and intelligent m-trail design approach for minimizing the number of m-trails and the total bandwidth consumed, which ubiquitously determines the length of the alarm code and bandwidth overhead for the m-trail deployment, respectively. In this paper, the m-trail design problem is investigated. To gain a deeper understanding of the problem, we first conduct a bound analysis on the minimum length of alarm code of each link required for UFL on the most sparse (i.e., ring) and dense (i.e., fully meshed) topologies. Then, a novel algorithm based on random code assignment (RCA) and random code swapping (RCS) is developed for solving the m-trail design problem. The algorithm is verified by comparison to an integer linear program (ILP) approach, and the results demonstrate its superiority in minimizing the fault management cost and bandwidth consumption while achieving significant reduction in computation time. To investigate the impact of topology diversity, extensive simulation is conducted on thousands of random network topologies with systematically increased network density.