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On the energy cost of robustness for green virtual network function placement in 5G virtualized infrastructures
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science. (Computer Networking, DISCO)ORCID iD: 0000-0001-8802-504X
Sorbonne Universités, Université de Technologie de Compiègne, France.
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science. (Computer Networking, DISCO)ORCID iD: 0000-0002-9446-8143
Universitat Politècnica de Catalunya, C. Barcelona, Spain.
2017 (English)In: Computer Networks, ISSN 1389-1286, E-ISSN 1872-7069, Vol. 125, p. 64-75Article in journal (Refereed) Published
Abstract [en]

Next generation 5G networks will rely on virtualized Data Centers (vDC) to host virtualized network functions on commodity servers. Such Network Function Virtualization (NFV) will lead to significant savings in terms of infrastructure cost and reduced management complexity. However, green strategies for networking and computing inside data centers, such as server consolidation or energy aware routing, should not negatively impact the quality and service level agreements expected from network operators. In this paper, we study how robust strategies that place virtual network functions (VNF) inside vDC impact the energy savings and the protection level against resource demand uncertainty. We propose novel optimization models that allow the minimization of the energy of the computing and network infrastructure which is hosting a set of service chains that implement the VNFs. The model explicitly provides for robustness to unknown or imprecisely formulated resource demand variations, powers down unused routers, switch ports and servers, and calculates the energy optimal VNF placement and network embedding also considering latency constraints on the service chains. We propose both exact and heuristic methods. Our experiments were carried out using the virtualized Evolved Packet Core (vEPC), which allows us to quantitatively assess the trade-off between energy cost, robustness and the protection level of the solutions against demand uncertainty. Our heuristic is able to converge to a good solution in a very short time, in comparison to the exact solver, which is not able to output better results in a longer run as demonstrated by our numerical evaluation. We also study the degree of robustness of a solution for a given protection level and the cost of additional energy needed because of the usage of more computing and network elements.

Place, publisher, year, edition, pages
Elsevier, 2017. Vol. 125, p. 64-75
Keyword [en]
Virtualization, Binary linear programming, Robust optimization, Network function virtualization (NFV), EPC, 5G
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kau:diva-64563DOI: 10.1016/j.comnet.2017.04.045OAI: oai:DiVA.org:kau-64563DiVA, id: diva2:1149545
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Funder
Knowledge Foundation, 4707
Available from: 2017-10-16 Created: 2017-10-16 Last updated: 2018-01-13Bibliographically approved

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