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Virtual Network Function Placement: Towards Minimizing Network Latency and Lead Time
Univ. of Sydney, Sydney, NSW, Australia.
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013). (DISCO, Computer Networking)ORCID iD: 0000-0001-9194-010X
School of Information Technologies, University of Sydney.
China Univ. of Geosci., China.
2017 (English)In: 2017 IEEE International Conference on Cloud Computing Technology and Science (CloudCom), Piscataway: IEEE, 2017, p. 90-97Conference paper, Published paper (Refereed)
Abstract [en]

Network Function Virtualization (NFV) is an emerging network architecture to increase flexibility and agility within operator's networks by placing virtualized services on demand in Cloud data centers (CDCs). One of the main challenges for the NFV environment is how to minimize network latency in the rapidly changing network environments. Although many researchers have already studied in the field of Virtual Machine (VM) migration and Virtual Network Function (VNF) placement for efficient resource management in CDCs, VNF migration problem for low network latency among VNFs has not been studied yet to the best of our knowledge. To address this issue in this article, we i) formulate the VNF migration problem and ii) develop a novel VNF migration algorithm called VNF Real-time Migration (VNF-RM) for lower network latency in dynamically changing resource availability. As a result of experiments, the effectiveness of our algorithm is demonstrated by reducing network latency by up to 70.90% after latency-aware VNF migrations.

Place, publisher, year, edition, pages
Piscataway: IEEE, 2017. p. 90-97
Keywords [en]
Network Function Virtualization, Virtual Network Function, Virtual Network Function Placement
National Category
Computer Sciences Software Engineering Computer Systems Telecommunications
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kau:diva-66137DOI: 10.1109/CLOUD.2017.118ISBN: 978-1-5386-1993-3 (electronic)ISBN: 978-1-5386-1994-0 (print)OAI: oai:DiVA.org:kau-66137DiVA, id: diva2:1180729
Conference
9th IEEE International Conference on Cloud Computing Technology and Science, 11-14 December 2017, Hong Kong
Available from: 2018-02-06 Created: 2018-02-06 Last updated: 2018-07-06Bibliographically approved

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Taheri, Javid

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CiteExportLink to record
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  • apa
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Output format
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