Pereira et al 2015a - Revision history

Scipediacontent at 15:23, 21 January 2021

2021-01-21T15:23:01Z

Scipediacontent: Scipediacontent moved page Draft Content 915230696 to Pereira et al 2015a

2020-10-14T12:38:04Z

Scipediacontent moved page Draft Content 915230696 to Pereira et al 2015a

Scipediacontent: Created page with " == Abstract == Traffic Engineering (TE) approaches are increasingly impor- tant in network management to allow an optimized configuration and resource allocation. In link-st..."

2020-10-14T12:38:02Z

Created page with " == Abstract == Traffic Engineering (TE) approaches are increasingly impor- tant in network management to allow an optimized configuration and resource allocation. In link-st..."

New page

== Abstract ==

Traffic Engineering (TE) approaches are increasingly impor- tant in network management to allow an optimized configuration and resource allocation. In link-state routing, the task of setting appropriate weights to the links is both an important and a challenging optimization task. A number of different approaches has been put forward towards this aim, including the successful use of Evolutionary Algorithms (EAs). In this context, this work addresses the evaluation of three distinct EAs, a single and two multi-objective EAs, in two tasks related to weight setting optimization towards optimal intra-domain routing, knowing the network topology and aggregated traffic demands and seeking to mini- mize network congestion. In both tasks, the optimization considers sce- narios where there is a dynamic alteration in the state of the system, in the first considering changes in the traffic demand matrices and in the latter considering the possibility of link failures. The methods will, thus, need to simultaneously optimize for both conditions, the normal and the altered one, following a preventive TE approach towards robust configurations. Since this can be formulated as a bi-objective function, the use of multi-objective EAs, such as SPEA2 and NSGA-II, came nat- urally, being those compared to a single-objective EA. The results show a remarkable behavior of NSGA-II in all proposed tasks scaling well for harder instances, and thus presenting itself as the most promising option for TE in these scenarios.

Document type: Part of book or chapter of book

== Full document ==
<pdf>Media:Draft_Content_915230696-beopen589-1476-document.pdf</pdf>

== Original document ==

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

* [http://hdl.handle.net/1822/38300 http://hdl.handle.net/1822/38300]

* [http://repositorium.sdum.uminho.pt/bitstream/1822/38300/1/2015-emo-vitor-pereira.pdf http://repositorium.sdum.uminho.pt/bitstream/1822/38300/1/2015-emo-vitor-pereira.pdf]

← Older revision		Revision as of 15:23, 21 January 2021
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	Traffic Engineering (TE) approaches are increasingly impor- tant in network management to allow an optimized configuration and resource allocation. In link-state routing, the task of setting appropriate weights to the links is both an important and a challenging optimization task. A number of different approaches has been put forward towards this aim, including the successful use of Evolutionary Algorithms (EAs). In this context, this work addresses the evaluation of three distinct EAs, a single and two multi-objective EAs, in two tasks related to weight setting optimization towards optimal intra-domain routing, knowing the network topology and aggregated traffic demands and seeking to mini- mize network congestion. In both tasks, the optimization considers sce- narios where there is a dynamic alteration in the state of the system, in the first considering changes in the traffic demand matrices and in the latter considering the possibility of link failures. The methods will, thus, need to simultaneously optimize for both conditions, the normal and the altered one, following a preventive TE approach towards robust configurations. Since this can be formulated as a bi-objective function, the use of multi-objective EAs, such as SPEA2 and NSGA-II, came nat- urally, being those compared to a single-objective EA. The results show a remarkable behavior of NSGA-II in all proposed tasks scaling well for harder instances, and thus presenting itself as the most promising option for TE in these scenarios.		Traffic Engineering (TE) approaches are increasingly impor- tant in network management to allow an optimized configuration and resource allocation. In link-state routing, the task of setting appropriate weights to the links is both an important and a challenging optimization task. A number of different approaches has been put forward towards this aim, including the successful use of Evolutionary Algorithms (EAs). In this context, this work addresses the evaluation of three distinct EAs, a single and two multi-objective EAs, in two tasks related to weight setting optimization towards optimal intra-domain routing, knowing the network topology and aggregated traffic demands and seeking to mini- mize network congestion. In both tasks, the optimization considers sce- narios where there is a dynamic alteration in the state of the system, in the first considering changes in the traffic demand matrices and in the latter considering the possibility of link failures. The methods will, thus, need to simultaneously optimize for both conditions, the normal and the altered one, following a preventive TE approach towards robust configurations. Since this can be formulated as a bi-objective function, the use of multi-objective EAs, such as SPEA2 and NSGA-II, came nat- urally, being those compared to a single-objective EA. The results show a remarkable behavior of NSGA-II in all proposed tasks scaling well for harder instances, and thus presenting itself as the most promising option for TE in these scenarios.
−
−	~~Document type: Part of book or chapter of book~~
−
−	~~== Full document ==~~
−	~~<pdf>Media:Draft_Content_915230696-beopen589-1476-document.pdf</pdf>~~


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	* [http://repositorium.sdum.uminho.pt/bitstream/1822/38300/1/2015-emo-vitor-pereira.pdf http://repositorium.sdum.uminho.pt/bitstream/1822/38300/1/2015-emo-vitor-pereira.pdf]		* [http://repositorium.sdum.uminho.pt/bitstream/1822/38300/1/2015-emo-vitor-pereira.pdf http://repositorium.sdum.uminho.pt/bitstream/1822/38300/1/2015-emo-vitor-pereira.pdf]
		+
		+	* [http://link.springer.com/content/pdf/10.1007/978-3-319-15892-1_39 http://link.springer.com/content/pdf/10.1007/978-3-319-15892-1_39],
		+	: [http://dx.doi.org/10.1007/978-3-319-15892-1_39 http://dx.doi.org/10.1007/978-3-319-15892-1_39]
		+
		+	* [https://link.springer.com/chapter/10.1007/978-3-319-15892-1_39 https://link.springer.com/chapter/10.1007/978-3-319-15892-1_39],
		+	: [http://repositorium.sdum.uminho.pt/bitstream/1822/38300/1/2015-emo-vitor-pereira.pdf http://repositorium.sdum.uminho.pt/bitstream/1822/38300/1/2015-emo-vitor-pereira.pdf],
		+	: [https://core.ac.uk/display/55638604 https://core.ac.uk/display/55638604],
		+	: [http://repositorium.sdum.uminho.pt/handle/1822/38300 http://repositorium.sdum.uminho.pt/handle/1822/38300],
		+	: [https://dblp.uni-trier.de/db/conf/emo/emo2015-2.html#PereiraSCRR15 https://dblp.uni-trier.de/db/conf/emo/emo2015-2.html#PereiraSCRR15],
		+	: [https://rd.springer.com/chapter/10.1007/978-3-319-15892-1_39 https://rd.springer.com/chapter/10.1007/978-3-319-15892-1_39],
		+	: [https://academic.microsoft.com/#/detail/570378233 https://academic.microsoft.com/#/detail/570378233]

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