Conference Paper

A New Heuristic for Monitoring Trail Allocation in All-Optical WDM Networks

Sch. of Commun. & Inf. Eng., Univ. of Electron. Sci. & Technol. of China, Chengdu, China
DOI: 10.1109/GLOCOM.2010.5683217 Conference: Global Telecommunications Conference (GLOBECOM 2010), 2010 IEEE
Source: IEEE Xplore

ABSTRACT We study the m-trail (monitoring trail) allocation problem in all-optical WDM mesh networks for achieving fast and unambiguous link failure localization. The existing ILP is not feasible for solving the problem in large-size networks. A heuristic RCA+RCS can find feasible solutions in a shorter running time, but it is a randomized algorithm. More importantly, RCA+RCS suffers from the disjoint trail problem which dramatically increases the number of required monitors in large-size networks. In this paper, we propose a new heuristic MTA (Monitoring Trail Allocation) to solve the problem. MTA avoids those issues in RCA+RCS, and achieves an efficient tradeoff between monitor cost and bandwidth cost. Compared with RCA+RCS, MTA greatly shortens the running time and achieves a much higher solution quality. We also show that MTA provides a flexible framework to enable multiple possible variations for future study.

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    ABSTRACT: 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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