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Optimizing resource allocation for virtualized network functions in a cloud center using genetic algorithms

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dc.contributor.author Rankothge, Windhya
dc.contributor.author Le, Franck
dc.contributor.author Russo, Alessandra
dc.contributor.author Lobo, Jorge
dc.date.accessioned 2019-05-13T10:06:46Z
dc.date.available 2019-05-13T10:06:46Z
dc.date.issued 2017
dc.identifier.citation Rankothge W, Le F, Russo A, Lobo J. Optimizing resource allocation for virtualized network functions in a cloud center using genetic algorithms. IEEE Trans Netw Serv Manage. 2017;14(2): 343-56. DOI: 10.1109/TNSM.2017.2686979
dc.identifier.issn 1932-4537
dc.identifier.uri http://hdl.handle.net/10230/37214
dc.description.abstract With the introduction of network function virtualization technology, migrating entire enterprise data centers into the cloud has become a possibility. However, for a cloud service provider (CSP) to offer such services, several research problems still need to be addressed. In previous work, we have introduced a platform, called network function center (NFC), to study research issues related to virtualized network functions (VNFs). In an NFC, we assume VNFs to be implemented on virtual machines that can be deployed in any server in the CSP network. We have proposed a resource allocation algorithm for VNFs based on genetic algorithms (GAs). In this paper, we present a comprehensive analysis of two resource allocation algorithms based on GA for: 1) the initial placement of VNFs and 2) the scaling of VNFs to support traffic changes. We compare the performance of the proposed algorithms with a traditional integer linear programming resource allocation technique. We then combine data from previous empirical analyses to generate realistic VNF chains and traffic patterns, and evaluate the resource allocation decision making algorithms. We assume different architectures for the data center, implement different fitness functions with GA, and compare their performance when scaling over the time.
dc.description.sponsorship This research was sponsored by U.S. Army Research Laboratory and U.K. Ministry of Defence and was accomplished under Agreement Number W911NF-06-3-0001. Jorge Lobo was partially supported by the Secretaria d'Universitats i Recerca de la Generalitat de Catalunya. Also this work was supported by the Maria de Maeztu Units of Excellence Programme and the Spanish Ministry of Economy and Competitiveness under the María de Maezto Units of Excellence Program (MDM-2015-0502).
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof IEEE Transactions on Network and Service Management. 2017;14(2): 343-56.
dc.rights © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The final published article can be found at http://ieeexplore.ieee.org/document/7885521/
dc.title Optimizing resource allocation for virtualized network functions in a cloud center using genetic algorithms
dc.type info:eu-repo/semantics/article
dc.identifier.doi http://dx.doi.org/10.1109/TNSM.2017.2686979
dc.subject.keyword Network function virtualization (NFV)
dc.subject.keyword Cloud resources optimization
dc.subject.keyword Genetic algorithms
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/acceptedVersion

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