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Automated Analysis and Profiling of VirtualNetwork Functions: the NFV-Inspector Approach
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013). (Datakommunikation, DISCO)ORCID iD: 0000-0002-4825-8831
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013). (Datakommunikation, DISCO)ORCID iD: 0000-0001-9194-010X
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013). (Datakommunikation, DISCO)ORCID iD: 0000-0002-9446-8143
R&D Technology and Industry, Ericsson, Sweden.
2018 (English)In: 2018 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN), IEEE, 2018Conference paper, Published paper (Refereed)
Abstract [en]

Discovering insights about Virtual Network Function (VNFs) resource demand characteristics will enable cloud vendors to optimize their underlying Network Function Virtualization (NFV) system orchestration and dramatically mitigate CapEx and OpEx spendings. However, analyzing large-scale NFV systems, especially in mobile network environments, is a challenging task and requires tailor-made approaches for each particular application. In this demo, we showcase NFV-Inspector, an open source and extensible VNF analysis platform that is capable of systematically benchmark and profile NFV deployments. Based on its pluggable framework, NFV-Inspector classifies VNFs resource demand characteristics and correlate their Key Performance Indicators (KPIs) with system-level Quality of Service (QoS) measurements. 

Place, publisher, year, edition, pages
IEEE, 2018.
Keywords [en]
Classification, Network Function Virtualization, Platform, Profiling, Quality of Service
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kau:diva-71388DOI: 10.1109/NFV-SDN.2018.8725697ISI: 000475896900023ISBN: 978-1-5386-8281-4 (electronic)ISBN: 978-1-5386-8282-1 (print)OAI: oai:DiVA.org:kau-71388DiVA, id: diva2:1292516
Conference
IEEE Conference on Network Function Virtulization and Software defined Networks, Verona, Italy, 27-29 November 2018
Projects
NFV Optimizer, 5276
Funder
Knowledge Foundation, 20160182
Note

Available from: 2019-02-28 Created: 2019-02-28 Last updated: 2019-08-06Bibliographically approved

Open Access in DiVA

fulltext(1021 kB)28 downloads
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Gokan Khan, MichelTaheri, JavidKassler, Andreas

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