Abstract

There are a group of problems in networking that can most naturally be described as optimization problems (network design, traffic engineering, etc.). There has been a great deal of research devoted to solving these problems, but this research has been concentrated on intra-domain problems where one network operator has complete information and control. An emerging field is inter-domain engineering, for instance, traffic engineering between large autonomous networks. Extending intra-domain optimization techniques to inter-domain problems is often impossible without the information available within a domain, and providers are often unwilling to share such information. This paper presents an alternative: we propose a method for traffic engineering that does not require sharing of important information across domains. The method extends the idea of genetic algorithms to allow symbiotic evolution between two parties. Both parties may improve their performance without revealing their data, other than what would be easily observed in any case. We show the method provides large reductions in network congestion, close to the optimal shortest path routing across a pair of networks. The results are highly robust to measurement noise, the method is very flexible, and it can be applied using existing routing.


Original document

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

http://link.springer.com/article/10.1007/s11235-010-9298-y/fulltext.html,
http://link.springer.com/content/pdf/10.1007/s11235-010-9298-y,
http://dx.doi.org/10.1007/s11235-010-9298-y under the license http://www.springer.com/tdm
http://www.cs.utexas.edu/~yzhang/papers/te-journal09.pdf,
https://link.springer.com/article/10.1007%2Fs11235-010-9298-y,
https://dblp.uni-trier.de/db/conf/valuetools/valuetools2008.html#RoughanZ08,
https://dl.acm.org/citation.cfm?id=1536970,
https://eudl.eu/doi/10.4108/ICST.VALUETOOLS2008.4500,
https://academic.microsoft.com/#/detail/2621353649
https://dblp.uni-trier.de/db/journals/telsys/telsys47.html#RoughanZ11,
https://link.springer.com/10.1007/s11235-010-9298-y,
https://dl.acm.org/citation.cfm?id=2728023,
https://rd.springer.com/article/10.1007%2Fs11235-010-9298-y,
https://academic.microsoft.com/#/detail/2037895657


DOIS: 10.4108/icst.valuetools2008.4500 10.1007/s11235-010-9298-y

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Published on 01/01/2010

Volume 2010, 2010
DOI: 10.4108/icst.valuetools2008.4500
Licence: Other

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