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Title:
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A Genetic Algorithm for VNF Provisioning in NFV-enabled Cloud/MEC RAN architectures
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Author:
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Ruiz, Lidia; J. Durán, Ramon; de Miguel, Ignacio; Sayyad Khodashenas, Pouria; Pedreno-Manresa, Jose-Juan; Merayo, Noemí; Aguado, Juan Carlos; Pavon-Marino, Pablo; Siddiqui, Shuaib; Mata, Javier; Fernández, Patricia; Lorenzo, Rubén M.; Avril, Evaristo J.
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Abstract:
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5G technologies promise to bring new network and service capacities and are expected to introduce significant architectural and service deployment transformations. The Cloud-Radio Access Networks (C-RAN) architecture, enabled by the combination of Software Defined Networking (SDN), Network Function Virtualization (NFV) and Mobile Edge Computing (MEC) technologies, play a key role in the development of 5G. In this context, this paper addresses the problems of Virtual Network Functions (VNF) provisioning (VNF-placement and service chain allocation) in a 5G network. In order to solve that problem, we propose a genetic algorithm that, considering both computing resources and optical network capacity, minimizes both the service blocking rate and CPU usage. In addition, we present an algorithm extension that adds a learning stage and evaluate the algorithm performance benefits in those scenarios where VNF allocations can be reconfigured. Results reveal and quantify the advantages of reconfiguring the VNF mapping depending on the current demands. Our methods outperform previous proposals in the literature, reducing the service blocking ratio while saving energy by reducing the number of active core CPUs |
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Publication date:
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2018-12-13 |
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Subject (UDC):
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621.3 - Enginyeria elèctrica. Electrotècnia. Telecomunicacions |
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Subject(s):
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5G / 6G & Internet of Things
Digital Technologies
Artificial Intelligence & Big Data
Software Networks |
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Rights:
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L'accés als continguts d'aquest document queda condicionat a l'acceptació de les condicions d'ús establertes per la següent llicència Creative Commons:http://creativecommons.org/licenses/by/4.0/ |
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Pages:
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18 p. |
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Document type:
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Article
Article - Published version |
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DOI:
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10.3390/app8122614
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Published by:
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Applied Sciences
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Collection:
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8;12
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Share:
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