A dataset for mobile edge computing network topologies

Mobile Edge Computing (MEC) is vital to support the numerous, future applications that are envisioned in the 5G and beyond mobile networks. Since computation capabilities are available at the edge of the network, applications that need ultra low-latency, high bandwidth and reliability can be deploye...

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Autores principales:	Bin Xiang, Jocelyne Elias, Fabio Martignon, Elisabetta Di Nitto
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	5G Network Mobile edge computing Base stations Network topology Geographic location Random graphs Computer applications to medicine. Medical informatics R858-859.7 Science (General) Q1-390
Acceso en línea:	https://doaj.org/article/fa2c2c8c18544377a3a4989c5d509765
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id	oai:doaj.org-article:fa2c2c8c18544377a3a4989c5d509765
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spelling	oai:doaj.org-article:fa2c2c8c18544377a3a4989c5d5097652021-11-14T04:33:32ZA dataset for mobile edge computing network topologies2352-340910.1016/j.dib.2021.107557https://doaj.org/article/fa2c2c8c18544377a3a4989c5d5097652021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2352340921008337https://doaj.org/toc/2352-3409Mobile Edge Computing (MEC) is vital to support the numerous, future applications that are envisioned in the 5G and beyond mobile networks. Since computation capabilities are available at the edge of the network, applications that need ultra low-latency, high bandwidth and reliability can be deployed more easily. This opens up the possibility of developing smart resource allocation approaches that can exploit the MEC infrastructure in an optimized way and, at the same time, fulfill the requirements of applications. However, up to date, the progress of research in this area is limited by the unavailability of publicly available true MEC topologies that could be used to run extensive experiments and to compare the performance on different solutions concerning planning, scheduling, routing etc. For this reason, we decided to infer and make publicly available several synthetic MEC topologies and scenarios.Specifically, based on the experience we have gathered with our experiments Xiang et al. [1], we provide data related to 3 randomly generated topologies, with increasing network size (from 25 to 100 nodes). Moreover, we propose a MEC topology generated from OpenCellID [2] real data and concerning the Base Stations’ location of 234 LTE cells owned by a mobile operator (Vodafone) in the center of Milan. We also provide realistic reference parameters (link bandwidth, computation and storage capacity, offered traffic), derived from real services provided by MEC in the deployment of 5G networks.Bin XiangJocelyne EliasFabio MartignonElisabetta Di NittoElsevierarticle5G NetworkMobile edge computingBase stationsNetwork topologyGeographic locationRandom graphsComputer applications to medicine. Medical informaticsR858-859.7Science (General)Q1-390ENData in Brief, Vol 39, Iss , Pp 107557- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	5G Network Mobile edge computing Base stations Network topology Geographic location Random graphs Computer applications to medicine. Medical informatics R858-859.7 Science (General) Q1-390
spellingShingle	5G Network Mobile edge computing Base stations Network topology Geographic location Random graphs Computer applications to medicine. Medical informatics R858-859.7 Science (General) Q1-390 Bin Xiang Jocelyne Elias Fabio Martignon Elisabetta Di Nitto A dataset for mobile edge computing network topologies
description	Mobile Edge Computing (MEC) is vital to support the numerous, future applications that are envisioned in the 5G and beyond mobile networks. Since computation capabilities are available at the edge of the network, applications that need ultra low-latency, high bandwidth and reliability can be deployed more easily. This opens up the possibility of developing smart resource allocation approaches that can exploit the MEC infrastructure in an optimized way and, at the same time, fulfill the requirements of applications. However, up to date, the progress of research in this area is limited by the unavailability of publicly available true MEC topologies that could be used to run extensive experiments and to compare the performance on different solutions concerning planning, scheduling, routing etc. For this reason, we decided to infer and make publicly available several synthetic MEC topologies and scenarios.Specifically, based on the experience we have gathered with our experiments Xiang et al. [1], we provide data related to 3 randomly generated topologies, with increasing network size (from 25 to 100 nodes). Moreover, we propose a MEC topology generated from OpenCellID [2] real data and concerning the Base Stations’ location of 234 LTE cells owned by a mobile operator (Vodafone) in the center of Milan. We also provide realistic reference parameters (link bandwidth, computation and storage capacity, offered traffic), derived from real services provided by MEC in the deployment of 5G networks.
format	article
author	Bin Xiang Jocelyne Elias Fabio Martignon Elisabetta Di Nitto
author_facet	Bin Xiang Jocelyne Elias Fabio Martignon Elisabetta Di Nitto
author_sort	Bin Xiang
title	A dataset for mobile edge computing network topologies
title_short	A dataset for mobile edge computing network topologies
title_full	A dataset for mobile edge computing network topologies
title_fullStr	A dataset for mobile edge computing network topologies
title_full_unstemmed	A dataset for mobile edge computing network topologies
title_sort	dataset for mobile edge computing network topologies
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/fa2c2c8c18544377a3a4989c5d509765
work_keys_str_mv	AT binxiang adatasetformobileedgecomputingnetworktopologies AT jocelyneelias adatasetformobileedgecomputingnetworktopologies AT fabiomartignon adatasetformobileedgecomputingnetworktopologies AT elisabettadinitto adatasetformobileedgecomputingnetworktopologies AT binxiang datasetformobileedgecomputingnetworktopologies AT jocelyneelias datasetformobileedgecomputingnetworktopologies AT fabiomartignon datasetformobileedgecomputingnetworktopologies AT elisabettadinitto datasetformobileedgecomputingnetworktopologies
_version_	1718429994625007616

A dataset for mobile edge computing network topologies

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