Matches in UGent Biblio for { <https://biblio.ugent.be/publication/1993809#aggregation> ?p ?o. }
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- aggregation classification "P1".
- aggregation creator person.
- aggregation creator person.
- aggregation creator person.
- aggregation creator person.
- aggregation creator person.
- aggregation creator person.
- aggregation date "2011".
- aggregation format "application/pdf".
- aggregation hasFormat 1993809.bibtex.
- aggregation hasFormat 1993809.csv.
- aggregation hasFormat 1993809.dc.
- aggregation hasFormat 1993809.didl.
- aggregation hasFormat 1993809.doc.
- aggregation hasFormat 1993809.json.
- aggregation hasFormat 1993809.mets.
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- aggregation hasFormat 1993809.xls.
- aggregation hasFormat 1993809.yaml.
- aggregation isPartOf urn:isbn:9781612842332.
- aggregation isPartOf urn:issn:1550-3607.
- aggregation language "eng".
- aggregation publisher "IEEE".
- aggregation rights "I have transferred the copyright for this publication to the publisher".
- aggregation subject "Technology and Engineering".
- aggregation title "Optimized channel and delay selection for contention resolution in optical networks".
- aggregation abstract "Both optical packet switching and optical burst switching provide viable alternatives to the current electronic switching in the backbone. To resolve contention, contiguous packets/bursts are sent over different channels, and/or provided with different Fiber Delay Line (FDL) buffer delays. Typically, the resulting channel and delay selection (CDS) algorithm bases its decision on the horizon value of the different channels. Up to now, a horizon algorithm known as MING (MINimal Gap) was assumed to provide minimal loss. Relying on Markov chain-based performance modeling and a Markov decision process (MDP), we reexamine the optimality of MING. This enables us to construct CDS algorithms that outperform MING, and this for any buffer size, traffic load and packet/burst size distribution. Further, we consider burst-size-dependent and load-dependent scheduling, two stochastic mechanisms that, together with preventive dropping, enhance the algorithm's performance. The paper presents the developed method in detail, together with detailed results of the performance gain realized.".
- aggregation authorList BK324237.
- aggregation aggregates 1995295.
- aggregation isDescribedBy 1993809.
- aggregation similarTo icc.2011.5963412.
- aggregation similarTo LU-1993809.