Reference : The Impact of Human Mobility on Edge Data Center Deployment in Urban Environments
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/40457
The Impact of Human Mobility on Edge Data Center Deployment in Urban Environments
English
Vitello, Piergiorgio [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit >]
Capponi, Andrea mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Fiandrino, Claudio [IMDEA Networks Institute]
Cantelmo, Guido [Technical University of Munich (TUM)]
Kliazovich, Dzmitry [ExaMotive]
Dec-2019
IEEE Global Communications Conference (GLOBECOM), Waikoloa, HI, USA, 2019
Yes
International
IEEE Global Communications Conference (GLOBECOM)
December 2019
Waikoloa, HI
USA
[en] edge computing ; data center deployment ; human mobility
[en] Multi-access Edge Computing (MEC) brings storage and computational capabilities at the edge of the network into so-called Edge Data Centers (EDCs) to better low-latency applications. To this end, effective placement of EDCs in urban environments is key for proper load balance and to minimize outages. In this paper, we specifically tackle this problem. To fully understand how the computational demand of EDCs varies, it is fundamental to analyze the complex dynamics of cities. Our work takes into account the mobility of citizens and their spatial patterns to estimate the optimal placement of MEC EDCs in urban environments in order to minimize outages. To this end, we propose and compare two heuristics. In particular, we present the mobility-aware deployment algorithm (MDA) that outperforms approaches that do not consider citizens mobility. Simulations are conducted in Luxembourg City by extending the CrowdSenSim simulator and show that efficient EDCs placement significantly reduces outages.
http://hdl.handle.net/10993/40457
FnR ; FNR12252781 > Andreas Zilian > DRIVEN > Data-driven Computational Modelling And Applications > 01/09/2018 > 28/02/2025 > 2017

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