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


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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    • "The study of [8] improved the algorithm in [2] by sequentially generating valid m-trails in a deterministic manner to shorten the running time. In particular, the scheme in [8] generates an m-trail by interconnecting some fragments using shortest paths, which are in turn constructed by picking some weighted links. The longest fragment will be chosen as the new m-trail if the fragments are still disconnected. "
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    ABSTRACT: Fast and unambiguous failure localization (UFL) is a desired feature in all-optical networks in order to provision high availability services. To save monitoring resources for signalling-free UFL, the paper studies a novel algorithm to localize any SRLG failure via monitoring-trails (m-trails) together with the working lightpaths. The feasibility conditions on the proposed m-trail allocation problem are discussed while different m-trail sharing strategies are introduced and compared via simulation. The problem of selecting an optimal set of working lightpaths is shown to be NP-complete and the simulation results verify the proposed algorithm with respect to monitoring resource consumption and running time.
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    ABSTRACT: Monitoring trails (m-trails) have been extensively studied as an alternative to the conventional link-based monitoring approach by using multi-hop supervisory lightpaths in all-optical networks. However, none of the previous studies have investigated the effect of length constraints upon the m-trail formation, which nonetheless correspond to the failure localization time. This paper addresses the above issue and formulates a new m-trail allocation problem, where the relationship between the number of m-trails versus the maximum hop count is explored. First, the paper investigates the theoretical bounds of allocating m-trails with at most k hops via an optimal group testing construction. Secondly, a novel meta-heuristic approach based on bacterial evolutionary algorithm for solving the length-constrained m-trail allocation problem is introduced. Through extensive simulations the performance gap of the proposed algorithm to the lower bound is presented on a wide diversity of topologies.
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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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