Optical network design and restoration

Doshi, Bharat T.; Dravida, Subrahmanyam; Harshavardhana, P.; Hauser, O.; Wang, Yufei

doi:10.1002/bltj.2147

Cited by 240 publications

(192 citation statements)

References 21 publications

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“…Survivability further increases the complexity of the design of optical WDM networks. Survivable design of WDM networks under static traffic was studied in [14], [15], [16], [17], [18], [19], [20], and [21]. Most of the current approaches solve the survivable routing problem in WDM networks either by directly solving complex ILP formulations that are extremely time consuming or by using heuristics or meta heuristics such as tabu-search, which do not provide any guarantee on the optimality of the solution obtained.…”

mentioning

confidence: 99%

Approximation Algorithms for Survivable Multicommodity Flow Problems with Applications to Network Design

Todimala

Ramamurthy

2006

Proceedings IEEE INFOCOM 2006. 25TH IEEE International Conference on Computer Communications

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Abstract-Multicommodity flow (MF) problems have a wide variety of applications in areas such as VLSI circuit design, network design, etc., and are therefore very well studied. The fractional MF problems are polynomial time solvable while integer versions are N P-complete. However, exact algorithms to solve the fractional MF problems have high computational complexity. Therefore approximation algorithms to solve the fractional MF problems have been explored in the literature to reduce their computational complexity. Using these approximation algorithms and the randomized rounding technique, polynomial time approximation algorithms have been explored in the literature.In the design of high-speed networks, such as optical wavelength division multiplexing (WDM) networks, providing survivability carries great significance. Survivability is the ability of the network to recover from failures.It further increases the complexity of network design and presents network designers with more formidable challenges.In this work we formulate the survivable versions of the MF problems. We build approximation algorithms for the survivable multicommodity flow (SMF) problems based on the framework of the approximation algorithms for the MF problems presented in [1] and [2]. We discuss applications of the SMF problems to solve survivable routing in capacitated networks.

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mentioning

confidence: 99%

Approximation Algorithms for Survivable Multicommodity Flow Problems with Applications to Network Design

Todimala

Ramamurthy

2006

Proceedings IEEE INFOCOM 2006. 25TH IEEE International Conference on Computer Communications

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“…The K successively shortest paths approach to restrict the size of the eligible path set is used in [3], [6], [9], [10]. Lagrangian relaxations with sub gradient optimization are used in [1], [19] to simplify the original hard problem. Many innovative schemes and heuristics have been reported in the literature [3], [6], [7], [10], [19]- [21].…”

Section: Related Workmentioning

confidence: 99%

“…The selection of an appropriate restoration scheme depends on many factors in addition to the spare capacity assignment cost. Some of the important performance criteria are: computational efficiency, restoration speed, complexity, and scalability [1]- [3]. The computational efficiency refers to the processing power and the memory required for the computation.…”

Section: Introductionmentioning

confidence: 99%

A comparative study of restoration schemes and spare capacity assignments in mesh networks

Patel

Chandrasekaran

Venkatesan

Proceedings. 12th International Conference on Computer Communications and Networks (IEEE Cat. No.03EX712)

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Abstract-This paper presents the results of a comparative study of spare capacity assignment for original quasi-path restoration (OQPR), improved quasi-path restoration (IQPR), link restoration (LR), path restoration (PR) and link-disjoint path restoration (LDPR) schemes. Numerical results indicate that the restoration schemes studied can be sorted from most expensive to least expensive (spare capacity assignment cost) in the following order: LR, OQPR, IQPR, LDPR and PR. Since IQPR is computationally very efficient, simpler than PR, scalable, and economical in spare capacity assignment, it provides a good alternative to PR when quick restoration is desired. However, due to the potential difficulty in rapid failure isolation coupled with the increasing importance of restoration speed and simplicity, LDPR is an attractive scheme. A number of networks with different topologies and projected traffic demand patterns are used in the experiments to study the effect of various network parameters on spare capacity assignment cost. The experimental analysis shows that network topology, demand patterns and the average number of hops per primary route have a significant impact on the spare capacity assignment cost savings offered by one scheme over the other.

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“…In Link Restoration [2], [5], [10], [12], traffic on the failed link is rerouted over a replacement path connecting the end-points of the failed link. In Path Restoration [4], [6], [8], each path disrupted by the link failure is considered separately and rerouted over an alternate path between the source and destination of that path. Link Restoration is easy to implement since only the two end nodes of the failed link are affected and the rerouting is localized but expensive in spare capacity [9].…”

Section: Introductionmentioning

confidence: 99%

Highly efficient spare capacity planning for generalized link restoration

Krishnamurthy¹,

Chandrasekaran²,

Venkatesan³

et al.

Proceedings. 12th International Conference on Computer Communications and Networks (IEEE Cat. No.03EX712)

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Abstract-We consider the problem of spare capacity allocation in mesh networks for link restoration. Only single link failures are considered and restored traffic is not split across multiple paths. In our model, links that are not part of the original transport network can also be used for restoration. This model, which is a generalized version, is different from the traditional model studied in literature. We present two heuristics to solve this problem and compare their performance with known lower bounds and the optimal solution. In numerous simulation experiments on randomly generated graphs containing up to 100 nodes, the heuristics often produced solutions within 3% of the lower bound for sparse graphs. The Integer Linear Programming solutions suggest that the heuristic solutions are closer to optimal than indicated by the deviation from the lower bounds. The results are optimal or near optimal (within 0.5% of the optimum values) for small graphs containing up to 8 nodes and 18 edges.

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Optical network design and restoration

Cited by 240 publications

References 21 publications

Approximation Algorithms for Survivable Multicommodity Flow Problems with Applications to Network Design

Approximation Algorithms for Survivable Multicommodity Flow Problems with Applications to Network Design

A comparative study of restoration schemes and spare capacity assignments in mesh networks

Highly efficient spare capacity planning for generalized link restoration

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