Multi-Layer traffic engineering through adaptive γ-path fragmentation and de-fragmentation

T. Cinkler, Péter Hegyi, Márk Asztalos, Géza Geleji, János Szigeti, András Kern

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

In Multi-Layer networks, where more than one layer is dynamic, i.e., connections are set up using not only the upper, e.g., IP layer but the underlying wavelength layer as well leads often to suboptimal performance due to long wavelength paths, that do not allow routing the traffic along the shortest path. The role of MLTE (Multi-Layer Traffic Engineering) is to cut these long wavelength paths into parts (fragments) that allow better routing at the upper layer (fragmentation), or to concatenate two or more fragments into longer paths (defragmentation) when the network load is low and therefore less hops are preferred. In this paper we present a new model (GG: Grooming Graph) and an algorithm for this model that supports Fragmentation and DeFragmentation of wavelength paths making the network always instantly adapt to changing traffic conditions. We introduce the notion of shadow capacities to model "lightpath tailoring". We implicitly assume that the wavelength paths carry such, e.g., IP traffic that can be interrupted for a few microseconds and that even allows minor packet reordering. To show the superior performance of our approach in various network and traffic conditions we have carried out an intensive simulation study.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages715-726
Number of pages12
Volume3976 LNCS
ISBN (Print)9783540341925, 3540341927, 9783540341925
DOIs
Publication statusPublished - 2006
Event5th International IFIP-TC6 Networking Conference, Networking 2006 - Coimbra, Portugal
Duration: May 15 2006May 19 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3976 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other5th International IFIP-TC6 Networking Conference, Networking 2006
CountryPortugal
CityCoimbra
Period5/15/065/19/06

Fingerprint

Traffic Engineering
Fragmentation
Multilayer
Wavelength
Path
Traffic
Fragment
Routing
Longest Path
Network layers
Reordering
Shortest path
Minor
Simulation Study
Model
Graph in graph theory

Keywords

  • Adaptive Multi-Layer Traffic Engineering
  • Grooming Graph
  • Wavelength Path Fragmentation and De-Fragmentation

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Cinkler, T., Hegyi, P., Asztalos, M., Geleji, G., Szigeti, J., & Kern, A. (2006). Multi-Layer traffic engineering through adaptive γ-path fragmentation and de-fragmentation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3976 LNCS, pp. 715-726). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3976 LNCS). Springer Verlag. https://doi.org/10.1007/11753810_60

Multi-Layer traffic engineering through adaptive γ-path fragmentation and de-fragmentation. / Cinkler, T.; Hegyi, Péter; Asztalos, Márk; Geleji, Géza; Szigeti, János; Kern, András.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 3976 LNCS Springer Verlag, 2006. p. 715-726 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3976 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Cinkler, T, Hegyi, P, Asztalos, M, Geleji, G, Szigeti, J & Kern, A 2006, Multi-Layer traffic engineering through adaptive γ-path fragmentation and de-fragmentation. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 3976 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3976 LNCS, Springer Verlag, pp. 715-726, 5th International IFIP-TC6 Networking Conference, Networking 2006, Coimbra, Portugal, 5/15/06. https://doi.org/10.1007/11753810_60
Cinkler T, Hegyi P, Asztalos M, Geleji G, Szigeti J, Kern A. Multi-Layer traffic engineering through adaptive γ-path fragmentation and de-fragmentation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 3976 LNCS. Springer Verlag. 2006. p. 715-726. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/11753810_60
Cinkler, T. ; Hegyi, Péter ; Asztalos, Márk ; Geleji, Géza ; Szigeti, János ; Kern, András. / Multi-Layer traffic engineering through adaptive γ-path fragmentation and de-fragmentation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 3976 LNCS Springer Verlag, 2006. pp. 715-726 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{391db86cf65c46c381310adba87d8e1b,
title = "Multi-Layer traffic engineering through adaptive γ-path fragmentation and de-fragmentation",
abstract = "In Multi-Layer networks, where more than one layer is dynamic, i.e., connections are set up using not only the upper, e.g., IP layer but the underlying wavelength layer as well leads often to suboptimal performance due to long wavelength paths, that do not allow routing the traffic along the shortest path. The role of MLTE (Multi-Layer Traffic Engineering) is to cut these long wavelength paths into parts (fragments) that allow better routing at the upper layer (fragmentation), or to concatenate two or more fragments into longer paths (defragmentation) when the network load is low and therefore less hops are preferred. In this paper we present a new model (GG: Grooming Graph) and an algorithm for this model that supports Fragmentation and DeFragmentation of wavelength paths making the network always instantly adapt to changing traffic conditions. We introduce the notion of shadow capacities to model {"}lightpath tailoring{"}. We implicitly assume that the wavelength paths carry such, e.g., IP traffic that can be interrupted for a few microseconds and that even allows minor packet reordering. To show the superior performance of our approach in various network and traffic conditions we have carried out an intensive simulation study.",
keywords = "Adaptive Multi-Layer Traffic Engineering, Grooming Graph, Wavelength Path Fragmentation and De-Fragmentation",
author = "T. Cinkler and P{\'e}ter Hegyi and M{\'a}rk Asztalos and G{\'e}za Geleji and J{\'a}nos Szigeti and Andr{\'a}s Kern",
year = "2006",
doi = "10.1007/11753810_60",
language = "English",
isbn = "9783540341925",
volume = "3976 LNCS",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "715--726",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

}

TY - GEN

T1 - Multi-Layer traffic engineering through adaptive γ-path fragmentation and de-fragmentation

AU - Cinkler, T.

AU - Hegyi, Péter

AU - Asztalos, Márk

AU - Geleji, Géza

AU - Szigeti, János

AU - Kern, András

PY - 2006

Y1 - 2006

N2 - In Multi-Layer networks, where more than one layer is dynamic, i.e., connections are set up using not only the upper, e.g., IP layer but the underlying wavelength layer as well leads often to suboptimal performance due to long wavelength paths, that do not allow routing the traffic along the shortest path. The role of MLTE (Multi-Layer Traffic Engineering) is to cut these long wavelength paths into parts (fragments) that allow better routing at the upper layer (fragmentation), or to concatenate two or more fragments into longer paths (defragmentation) when the network load is low and therefore less hops are preferred. In this paper we present a new model (GG: Grooming Graph) and an algorithm for this model that supports Fragmentation and DeFragmentation of wavelength paths making the network always instantly adapt to changing traffic conditions. We introduce the notion of shadow capacities to model "lightpath tailoring". We implicitly assume that the wavelength paths carry such, e.g., IP traffic that can be interrupted for a few microseconds and that even allows minor packet reordering. To show the superior performance of our approach in various network and traffic conditions we have carried out an intensive simulation study.

AB - In Multi-Layer networks, where more than one layer is dynamic, i.e., connections are set up using not only the upper, e.g., IP layer but the underlying wavelength layer as well leads often to suboptimal performance due to long wavelength paths, that do not allow routing the traffic along the shortest path. The role of MLTE (Multi-Layer Traffic Engineering) is to cut these long wavelength paths into parts (fragments) that allow better routing at the upper layer (fragmentation), or to concatenate two or more fragments into longer paths (defragmentation) when the network load is low and therefore less hops are preferred. In this paper we present a new model (GG: Grooming Graph) and an algorithm for this model that supports Fragmentation and DeFragmentation of wavelength paths making the network always instantly adapt to changing traffic conditions. We introduce the notion of shadow capacities to model "lightpath tailoring". We implicitly assume that the wavelength paths carry such, e.g., IP traffic that can be interrupted for a few microseconds and that even allows minor packet reordering. To show the superior performance of our approach in various network and traffic conditions we have carried out an intensive simulation study.

KW - Adaptive Multi-Layer Traffic Engineering

KW - Grooming Graph

KW - Wavelength Path Fragmentation and De-Fragmentation

UR - http://www.scopus.com/inward/record.url?scp=33745909135&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=33745909135&partnerID=8YFLogxK

U2 - 10.1007/11753810_60

DO - 10.1007/11753810_60

M3 - Conference contribution

SN - 9783540341925

SN - 3540341927

SN - 9783540341925

VL - 3976 LNCS

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 715

EP - 726

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

PB - Springer Verlag

ER -