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dc.contributor.authorVitello, Piergiorgio
dc.contributor.authorCapponi, Andrea
dc.contributor.authorFiandrino, Claudio 
dc.contributor.authorCantelmo, Guido
dc.date.accessioned2021-07-13T09:50:21Z
dc.date.available2021-07-13T09:50:21Z
dc.date.issued2021-07
dc.identifier.isbn9780367814397
dc.identifier.urihttp://hdl.handle.net/20.500.12761/990
dc.description.abstractThe objective of Multi-access Edge Computing (MEC) is to better support lowlatency applications by bringing storage and computational capabilities at the edge of the network into so-called Edge Data Centers (EDCs). To this end, effective placement of EDCs in urban environments is key for proper load balancing, outage minimization and energy efficiency. This chapter tackles this problem and takes into account the mobility of citizens and their spatial patterns to estimate the optimal placement of MEC EDCs in urban environments that minimizes out- ages and energy efficiency. First, the chapter will discuss how the computational demand and user mobility affect EDC placement and expose three heuristics as solutions. These methods are validated with Crowd EdgeSim, a simulator build specifically for such problem and show that efficient EDCs placement significantly reduces outages.
dc.publisherCRC Press
dc.titleMobility-Aware Solutions for Edge Data Center Deployment in Urban Environments
dc.typebook part
dc.book.titleData Science and Big Data Analytics in Smart Environments
dc.description.refereedTRUE
dc.description.statuspub
dc.eprint.idhttp://eprints.networks.imdea.org/id/eprint/2347


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