ML-based performance prediction of SDN using simulated data from real and synthetic networks

to connected objects

Location: Deutsche Nationalbibliothek Frankfurt am Main

Extent: Online-Ressource

Language: Englisch

Notes: In: NOMS 2022 - 2022 IEEE/IFIP Network Operations and Management Symposium, April 25-29, 2022, Budapest, Hungary, S. 1-7

Classification: Informatik

Event: Veröffentlichung

(where): Augsburg

(who): Universität Augsburg

(when): 2022

Event: Veröffentlichung

(where): Piscataway, NJ

(who): IEEE

(when): 2022

Creator: Dietz, Katharina
Gray, Nicholas
Seufert, Michael Thomas
Hoßfeld, Tobias

Contributor: Varga, Pal
Zambenedetti Granville, Lisandro
Galis, Alex
Godor, Istvan
Limam, Noura
Chemouil, Prosper
François, Jérôme
Pahl, Marc-Oliver

DOI: 10.1109/noms54207.2022.9789916

URN: urn:nbn:de:bvb:384-opus4-1072545

Rights: Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 25.03.2025, 1:49 PM CET

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Associated

Dietz, Katharina
Gray, Nicholas
Seufert, Michael Thomas
Hoßfeld, Tobias
Varga, Pal
Zambenedetti Granville, Lisandro
Galis, Alex
Godor, Istvan
Limam, Noura
Chemouil, Prosper
François, Jérôme
Pahl, Marc-Oliver
Universität Augsburg
IEEE

Time of origin

2022

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ML-based performance prediction of SDN using simulated data from real and synthetic networks

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Other Objects (12)

Technical Report on DFG Project SDN-App: SDN-enabled Application-aware Network Control Architectures and their Performance Assessment

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Simulative performance evaluation of a mobile peer-to-peer file-sharing system : Report Nr. 345

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White Paper on Crowdsourced Network and QoE Measurements – Definitions, Use Cases and Challenges

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On Performance Assessment of Control Mechanisms and Virtual Components in SDN-based Networks

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