Spare Capacity Planning for Survivable Mesh Networks

Al-Rumaih, Adel; Tipper, David; Liu, Yu; Norman, Bryan A.

doi:10.1007/3-540-45551-5_80

Cited by 14 publications

(8 citation statements)

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“…The approach employed by [19] is based on a genetic algorithm. The algorithm utilizes crossover and mutation operators to evolve "good" solutions toward optimality.…”

Section: Related Workmentioning

confidence: 99%

Maximizing Restorable Throughput in MPLS Networks

Cohen

Nakibly

2008

2008 Proceedings IEEE INFOCOM - The 27th Conference on Computer Communications

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Abstract-MPLS recovery mechanisms are increasing in popularity because they can guarantee fast restoration and high QoS assurance. Their main advantage is that their backup paths are established in advance, before a failure event takes place. Most research on the establishment of primary and backup paths has focused on minimizing the added capacity required by the backup paths in the network. However, this so-called Spare Capacity Allocation (SCA) metric is less practical for network operators who have a fixed capacitated network and want to maximize their revenues. In this paper, we present a comprehensive study on restorable throughput maximization in MPLS networks. We present the first polynomial-time algorithms for the splittable version of the problem. For the unsplittable version, we provide a lower bound for the approximation ratio and propose an approximation algorithm with an almost identical bound. We present an efficient heuristic which is shown to have excellent performance. One of our most important conclusions is that when one seeks to maximize revenue, local recovery should be the recovery scheme of choice.

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“…The approach employed by [19] is based on a genetic algorithm. The algorithm utilizes crossover and mutation operators to evolve "good" solutions toward optimality.…”

Section: Related Workmentioning

confidence: 99%

Maximizing Restorable Throughput in MPLS Networks

Cohen

Nakibly

2008

2008 Proceedings IEEE INFOCOM - The 27th Conference on Computer Communications

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“…Some of these heuristics have been based on the genetic principles [3]. According to these principles, search loops are created to look for the best possible topology solution that satisfies the required design criterion.…”

Section: Introductionmentioning

confidence: 99%

A tutorial on using genetic algorithms for the design of network topology

2006

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SUMMARYThe design of network topology is an important part of network design, since network topology is directly associated with network operational behavior, capacity, reliability, and cost. This paper is a tutorial paper concerned with illustrating how the optimization capabilities of genetic algorithms can be used to design suitable network topologies considering basic topology problems. Simple genetic algorithms have been developed for the topology problem of mesh networks, considering single node and single link failure tolerance. The algorithms are based on criteria of two important measures: minimizing the length of communication links; and minimizing traffic flow through these links for given traffic loads. The first measure contributes to minimizing the cost of cabling, while the second measure contributes to minimizing the cost of link capacity. The work provides a useful approach and tools to network students and professionals concerned with the topology design of backbone networks. The developed software is made available on the Internet.

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“…Networks based on SONET, and digital cross-connect systems fall under the STM category. In [1,2], we consider the problem of given a STM mesh type network topology, the normal traffic demand, and the capacity allocation to meet the normal traffic demand, how much spare capacity should be provisioned and where should it be located in order for the network to tolerate a specified set of failure scenarios (e.g., loss of any single link). The term "mesh" does not imply that the network topology is a full mesh, but rather that the network nodes are at least two connected.…”

Section: Survivable Stm Network Designmentioning

confidence: 99%

“…The description of the proposed methodology is given in detail in [1,2] along with a study of numerical results for a variety of network topologies, illustrating the application of the proposed genetic algorithm technique, guidelines for parameter selection and analysis of the computational complexity. Additionally, for the sake of comparison, numerical results for small networks are given for the standard integer programming approaches and a popular heuristic from the literature.…”

Section: Survivable Stm Network Designmentioning

confidence: 99%

“…In particular Adel Al-Rumaih developed the genetic algorithm based spare capacity planning method and its implementation into a design tool in his Ph.D. dissertation [1]. Graduate student Yu Liu has contributed to the relative comparison study of the genetic algorithm approach and mathematical programming techniques reported in [2]. Ph.D. candidate Anotai Srikitja developed the extended version of the NS simulator and conducted the study on survivable service in NGI reported in [30].…”

Section: University Of Pittsburghmentioning

confidence: 99%

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