Signaling free localization of node failures in all-optical networks

Tapolcai, János; Rónyai, Lajos; Hosszú, Éva; Ho, Pin Han; Subramaniam, Suresh

doi:10.1109/infocom.2014.6848125

Cited by 6 publications

(9 citation statements)

References 21 publications

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“…As a comparison we used the Greedy Link Swapping heuristic for node failures GLS node in [8]. The objective in the GLS node heuristic is to minimize the total number of m-trails, while in this scenario the objective is to minimize the average number of m-trails seen at each node of the network.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…The m-trail approach is expected to serve as a complement to the existing electronic signaling approaches and enables an ultrafast and deterministic fault management process [1]- [8]. A detailed overview of fast failure localization with the m-trails can be found in the book [2].…”

Section: Introductionmentioning

confidence: 99%

“…However, for link failures the efficient heuristics allocate only m-trails that traverse every node in the network, which is not capable of localizing node failures, as a failure of the node would interrupt each m-trail in the network. Localizing node failures in NL-UFL is a rather challenging problem and the only paper reported was [8]. It presents a heuristic algorithm that generates a random initial solution that is possibly invalid.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper we follow the NL-UFL model of [8] and the goal is to localize single node failures only. Note that the mtrails can localize a single failure, thus to be able to identify a next failure after the first failure the m-trails should be reconfigured.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A heuristic algorithm for network-wide local unambiguous node failure localization

Gyimothi

Tapolcai

2015

2015 IEEE 16th International Conference on High Performance Switching and Routing (HPSR)

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This paper deals with fast node failure localization in optical networks with monitoring trails (m-trails). It is based on Network-wide local unambiguous failure localization, which enables every node to autonomously localize any single node failure in the network in a distributed and all-optical manner by inspecting the m-trails traversing through the node. A new and innovative heuristic algorithm is presented which is based on recursion and constrained matching algorithms in general graphs. Extensive simulation is conducted to examine the proposed heuristic in terms of the required cover length to achieve NL-UFL. In our experiments the new heuristic can reduce the computation time by 1000-10000 times compared to prior art.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A heuristic algorithm for network-wide local unambiguous node failure localization

Gyimothi

Tapolcai

2015

2015 IEEE 16th International Conference on High Performance Switching and Routing (HPSR)

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“…To the best of our knowledge, [5] is the only paper reported on node failures in optical backbone networks. It presents a heuristic algorithm called GLS node that can solve the NL-UFL problem for single link and single node failures.…”

Section: Related Workmentioning

confidence: 99%

Constructions for unambiguous node failure localization in grid topologies

Gyimothi

Hosszú

Tapolcai

2015

2015 7th International Workshop on Reliable Networks Design and Modeling (RNDM)

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Precise, fast and scalable fault localization is a highly desired feature in all optical mesh networks. The monitoring trail (m-trail) framework has been long in use for centralized failure localization, its capabilities were recently utilized to accommodate the distributed scenario as well. Most of the prior art focused solely on link failures; in this study we step further and analyze node failures only. Node failures are fundamentally different from link failures, and thus a new theoretical framework is developed. In particular, we are interested in the scalability of localizing node failures. From an information theoretic view, with b m-trails at most 2 b − 1 single failures can be identified. This intuitively leads to a very attractive property that the number of monitors might be logarithmic to the size of the network. Does this logarithmic behaviour holds for real life topologies too? As a step towards the answer, we show tight constructions for both centralized and distributed node failure localization.

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Smart elements in combinatorial group testing problems

Gerbner

Vizer

2018

J Comb Optim

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In combinatorial group testing problems the questioner needs to find a special element x ∈ [n] by testing subsets of [n]. Tapolcai et al. [27,28] introduced a new model, where each element knows the answer for those queries that contain it and each element should be able to identify the special one.Using classical results of extremal set theory we prove that if F n ⊆ 2 [n] solves the nonadaptive version of this problem and has minimal cardinality, then lim n→∞ |F n | log 2 n = log (3/2) 2.This improves results in [27,28].We also consider related models inspired by secret sharing models, where the elements should share information among them to find out the special one. Finally the adaptive versions of the different models are investigated.and for F ⊆ [n] the answer is the appropriate value of the function t :The tested subsets are called queries and the special element is usually called defective in the group testing literature. Questioner's aim is to ask as few queries as possible and the number of queries needed to ask in the worst case is called the worst-case complexity of the problem. For any combinatorial group testing problem there are at least two main approaches: whether it is adaptive or non-adaptive. In the adaptive scenario Questioner asks queries depending on the answers for the previously asked queries, however in the non-adaptive version Questioner must pose all the queries at the beginning.Let us briefly describe the solution for the (above mentioned) most basic combinatorial group testing model in the non-adaptive case. We call a family F ⊆ 2 [n] separating if for any two different x, y ∈ [n] there is F ∈ F with x ∈ F and y ∈ F, or y ∈ F and x ∈ F.Fact 1. Questioner finds the defective by asking elements of F ⊆ 2 [n] if and only if F is separating.The notion of separating family in the context of combinatorial group testing was introduced and first studied by Rényi in [23]. We will also use the following simple fact later:Fact 2. Suppose F n ⊆ 2 [n] is the smallest separating family. Then we have:|F n | = ⌈log 2 n⌉.One can imagine many possible generalizations of the most basic classical model: more defectives, other answers (threshold [6] or density [13] group testing), average case complexity [4], rounds [7, 15]. For a survey on different non-adaptive models see e.g. [10]. Combinatorial group testing problems were first considered during the World War II by Dorfman [9] in the context of mass blood testing. Since then group testing techniques have had many different applications, for example in fault diagnosis in optical networks [17], in quality control in product testing [25] or failure detection in wireless sensor networks [21]. New feature of the elementsInspired by the node failure localization model of Tapolcai et al. [27,28] we introduce a possible new feature of the elements. Informally speaking an element can be kind of smart and this fact means two things:

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Signaling free localization of node failures in all-optical networks

Cited by 6 publications

References 21 publications

A heuristic algorithm for network-wide local unambiguous node failure localization

A heuristic algorithm for network-wide local unambiguous node failure localization

Constructions for unambiguous node failure localization in grid topologies

Smart elements in combinatorial group testing problems

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