Smart bandwidth allocation for next generation networks adopting software-defined network approach

This data article contains information on a new intelligent bandwidth allocation model for future network (Smart Allocation). The included data describe the topology of the network testbed and the obtained results. Obtained data show the effectiveness of the proposed model in comparison with the MAM...

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Main Authors: Ayoub Bahnasse, Fatima Ezzahraa Louhab, Hafsa Ait Oulahyane, Mohamed Talea, Assia Bakali
Format: Article
Language:English
Published: Elsevier 2018-10-01
Series:Data in Brief
Online Access:http://www.sciencedirect.com/science/article/pii/S2352340918309430
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author Ayoub Bahnasse
Fatima Ezzahraa Louhab
Hafsa Ait Oulahyane
Mohamed Talea
Assia Bakali
author_facet Ayoub Bahnasse
Fatima Ezzahraa Louhab
Hafsa Ait Oulahyane
Mohamed Talea
Assia Bakali
author_sort Ayoub Bahnasse
collection DOAJ
description This data article contains information on a new intelligent bandwidth allocation model for future network (Smart Allocation). The included data describe the topology of the network testbed and the obtained results. Obtained data show the effectiveness of the proposed model in comparison with the MAM and RDM bandwidth allocation models. In relation to the performances evaluation, a variety of flows are used such as: voice over IP (VoIP), video, HTTP, and Internet Control Message Protocol (ICMP). The evaluation criteria are: VoIP latency and jitter, Peak Signal to Noise Ratio (PSNR) video, retransmission video, goodput, HTTP response page, and the Round-Trip Time (RTT) ICMP delay. The presented data are extracted based on simulation.
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spelling doaj.art-0f0bf3459b8e4c22ae86e07cd7774cd72022-12-22T00:22:42ZengElsevierData in Brief2352-34092018-10-0120840845Smart bandwidth allocation for next generation networks adopting software-defined network approachAyoub Bahnasse0Fatima Ezzahraa Louhab1Hafsa Ait Oulahyane2Mohamed Talea3Assia Bakali4Lab LTI, Faculty of Sciences Ben M׳SIK, University Hassan II of Casablanca, Morocco; Corresponding author.Lab LTI, Faculty of Sciences Ben M׳SIK, University Hassan II of Casablanca, MoroccoLab LTI, Faculty of Sciences Ben M׳SIK, University Hassan II of Casablanca, MoroccoLab LTI, Faculty of Sciences Ben M׳SIK, University Hassan II of Casablanca, MoroccoNaval Royal School, Casablanca, MoroccoThis data article contains information on a new intelligent bandwidth allocation model for future network (Smart Allocation). The included data describe the topology of the network testbed and the obtained results. Obtained data show the effectiveness of the proposed model in comparison with the MAM and RDM bandwidth allocation models. In relation to the performances evaluation, a variety of flows are used such as: voice over IP (VoIP), video, HTTP, and Internet Control Message Protocol (ICMP). The evaluation criteria are: VoIP latency and jitter, Peak Signal to Noise Ratio (PSNR) video, retransmission video, goodput, HTTP response page, and the Round-Trip Time (RTT) ICMP delay. The presented data are extracted based on simulation.http://www.sciencedirect.com/science/article/pii/S2352340918309430
spellingShingle Ayoub Bahnasse
Fatima Ezzahraa Louhab
Hafsa Ait Oulahyane
Mohamed Talea
Assia Bakali
Smart bandwidth allocation for next generation networks adopting software-defined network approach
Data in Brief
title Smart bandwidth allocation for next generation networks adopting software-defined network approach
title_full Smart bandwidth allocation for next generation networks adopting software-defined network approach
title_fullStr Smart bandwidth allocation for next generation networks adopting software-defined network approach
title_full_unstemmed Smart bandwidth allocation for next generation networks adopting software-defined network approach
title_short Smart bandwidth allocation for next generation networks adopting software-defined network approach
title_sort smart bandwidth allocation for next generation networks adopting software defined network approach
url http://www.sciencedirect.com/science/article/pii/S2352340918309430
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