Cinkler et al 2006a - Revision history

Scipediacontent at 12:23, 21 January 2021

2021-01-21T12:23:25Z

Scipediacontent: Scipediacontent moved page Draft Content 673161157 to Cinkler et al 2006a

2020-10-14T11:53:04Z

Scipediacontent moved page Draft Content 673161157 to Cinkler et al 2006a

Scipediacontent: Created page with " == 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 wavele..."

2020-10-14T11:53:01Z

Created page with " == 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 wavele..."

New page

== 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.\r \r In this paper we present a new model (GG: Grooming Graph) and an algorithm for this model that supports Fragmentation and De-Fragmentation 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.\r \r To show the superior performance of our approach in various network and traffic conditions we have carried out an intensive simulation study.

Document type: Part of book or chapter of book

== Full document ==
<pdf>Media:Draft_Content_673161157-beopen407-1578-document.pdf</pdf>

== Original document ==

The different versions of the original document can be found in:

* [https://link.springer.com/content/pdf/10.1007%2F11753810_60.pdf https://link.springer.com/content/pdf/10.1007%2F11753810_60.pdf]

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 == 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.\r \r In this paper we present a new model (GG: Grooming Graph) and an algorithm for this model that supports Fragmentation and De-Fragmentation 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.\r \r To show the superior performance of our approach in various network and traffic conditions we have carried out an intensive simulation study.
+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 De-Fragmentation of wavelength paths making the network always instantly adapt to changing traffic conditions. We introduce the notion of shadow capacities to model âlightpath tailoringâ. 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.
-−
-Document type: Part of book or chapter of book
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-== Full document ==
-<pdf>Media:Draft_Content_673161157-beopen407-1578-document.pdf</pdf>
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 * [https://link.springer.com/content/pdf/10.1007%2F11753810_60.pdf https://link.springer.com/content/pdf/10.1007%2F11753810_60.pdf]

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