Fault restoration and spare capacity allocation with QoS constraints for MPLS networks

Oh, Taegeun; Chen, T.M.; Kennington, Jeffery L.

doi:10.1109/glocom.2000.891932

Cited by 17 publications

(3 citation statements)

References 10 publications

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“…RELATED WORK Spare capacity assignment is an important part in dimensioning a survivable network and has been studied extensively. The spare capacity assignment problem has been formulated as an (integer) linear program using multi-commodity flow model in [8], [14], [15], [23]- [26]. These models use the pre-defined eligible path sets for all the demand pairs to formulate the search space.…”

Section: ) Case I: Path a Is Identified By The First Max-flow Algorimentioning

confidence: 99%

Improved Quasi-Path Restoration in Mesh Networks

Patel

Chandrasekaran

Venkatesan

2008

IEEE/ACM Trans. Networking

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Restoration of disrupted traffic is critical in today's high-speed self-healing telecommunication networks. A restoration scheme dynamically discovers alternate paths bypassing the failed component. This paper presents an (online) improved quasi-path restoration (IQPR) scheme. IQPR is derived from the two-commodity max-flow algorithm. The running time complexity of IQPR is ( 3 ). Therefore, IQPR is computationally more efficient and more scalable than path restoration (PR). IQPR is faster (in restoration speed) and less complex than PR, and more economical (in spare capacity requirement) than link restoration (LR). Thus, it provides a good alternative to PR when quick restoration of disrupted traffic is desired. The (offline) spare capacity planning problem deals with the allocation of spare capacity to each link in the network, such that the spare capacity requirement is minimized, while guaranteeing the desired level of restoration in the event of a link failure. The spare capacity allocation problems for LR, original quasi-path restoration (OQPR), IQPR, link-disjoint path restoration (LDPR) and PR are formulated as integer linear programming problems. Numerical results illustrate that the restoration schemes studied can be sorted from the least efficient to the most efficient (in the spare capacity requirement) in the following order: LR, OQPR, IQPR, LDPR and PR.The experimental analysis shows that network topology and demand patterns have a significant impact on the spare capacity savings offered by one scheme over the other. Merits and demerits of these schemes are also discussed.

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Section: ) Case I: Path a Is Identified By The First Max-flow Algorimentioning

confidence: 99%

Improved Quasi-Path Restoration in Mesh Networks

Patel

Chandrasekaran

Venkatesan

2008

IEEE/ACM Trans. Networking

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“…Nonetheless, in the case of restoration, only a rough estimate of the probability that a particular connection can be somehow recovered could be considered (e.g. [14]). Although this estimate is rough, it could be more complex to calculate than an exact value of the availability for pro-active methods.…”

Section: Abstractionmentioning

confidence: 99%

A unified quality of recovery (QoR) measure

Chołda

Jajszczyk

Wajda

2007

Int J Communication

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“…A backup route selection algorithm based on optimization of equivalent bandwidth is formulated and demonstrated for an example network. The complete manuscript may be found in Oh et al (2000),…”

Section: Mpls Routingmentioning

confidence: 99%

Optimization-Based Techniques for Designing Optical Networks

Kennington¹

2003

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Fault restoration and spare capacity allocation with QoS constraints for MPLS networks

Cited by 17 publications

References 10 publications

Improved Quasi-Path Restoration in Mesh Networks

Improved Quasi-Path Restoration in Mesh Networks

A unified quality of recovery (QoR) measure

Optimization-Based Techniques for Designing Optical Networks

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