A genetic algorithm approach for service function chain placement in 5G and beyond, virtualized edge networks

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dc.contributor.author Magoula, Lina
dc.contributor.author Barmpounakis, Sokratis
dc.contributor.author Stavrakakis, Ioannis
dc.contributor.author Alonistioti, Nancy
dc.date.accessioned 2022-02-10T10:01:26Z
dc.date.issued 2021-08-04
dc.identifier.bibliographicCitation Magoula, L., Barmpounakis, S., Stavrakakis, I. & Alonistioti, N. (2021). A genetic algorithm approach for service function chain placement in 5G and beyond, virtualized edge networks. Computer Networks, 195, 108157.
dc.identifier.issn 1389-1286
dc.identifier.uri http://hdl.handle.net/10016/34088
dc.description.abstract Network Function Virtualization (NFV) is already considered as a structural enabler of today’s networking technology and particularly the 5th Generation of Broadband and Cellular Networks (5G). NFV provides the means to flexibly and dynamically manage and allocate resources, without being restricted to the hardware limitations of the network/cloud infrastructure. Resource orchestration for specific 5G vertical industries and use case families, such as Industry 4.0 and Industrial Internet of Things (IIoT), often introduce very strict requirements in terms of network performance. In such a dynamic environment, the challenge is to efficiently place directed graphs of Virtual Network Functions (VNFs), named as SFCs (Service Function Chains), to the underlying network topology and to dynamically allocate the required resources. To this end, this work presents a novel framework, which makes use of a delay and location aware Genetic Algorithm (GA)-based approach, in order to perform optimized sequential SFC placement. Evaluation results clearly demonstrate the effectiveness of the proposed framework in terms of producing solutions that approximate well the global optimal, as well as achieving low execution time due to the employed GA-based approach and the incorporation of an early stopping criterion. The performance benefits of the proposed framework are evaluated in the context of an extensive set of simulation-based scenarios, under diverse network configurations and scales.
dc.description.sponsorship This research has been partially funded by EC H2020 5GPPP 5Growth project (Grant number: 856709).
dc.format.extent 12
dc.language.iso eng
dc.publisher Elsevier
dc.rights © 2021 Elsevier B.V. All rights reserved.
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.other Resource orchestration
dc.subject.other Genetic algorithm
dc.subject.other Network function virtualization
dc.title A genetic algorithm approach for service function chain placement in 5G and beyond, virtualized edge networks
dc.type article
dc.subject.eciencia Telecomunicaciones
dc.identifier.doi https://doi.org/10.1016/j.comnet.2021.108157
dc.rights.accessRights embargoedAccess
dc.relation.projectID info:eu-repo/grantAgreement/EC/856709
dc.type.version acceptedVersion
dc.identifier.publicationfirstpage 1
dc.identifier.publicationissue 108157
dc.identifier.publicationlastpage 12
dc.identifier.publicationtitle Computer Networks
dc.identifier.publicationvolume 195
carlosiii.embargo.liftdate 2023-08-04
carlosiii.embargo.terms 2023-08-04
dc.contributor.funder European Commission
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