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dc.contributor.authorMiori L
dc.contributor.authorSanin J
dc.contributor.authorHelmer S
dc.contributor.editor
dc.date.accessioned2018-05-24T15:10:21Z
dc.date.available2018-05-24T15:10:21Z
dc.date.issued2017
dc.identifier.isbn978-3-319-60794-8
dc.identifier.issn0302-9743
dc.identifier.urihttp://dx.doi.org/10.1007/978-3-319-60795-5_16
dc.identifier.urihttps://link.springer.com/chapter/10.1007/978-3-319-60795-5_16
dc.identifier.urihttp://hdl.handle.net/10863/4784
dc.description.abstractSmall credit-card-sized single-board computers, such as the Raspberry Pi, are becoming ever more popular in areas unrelated to the education of children, for which they were originally intended. So far, these computers have mainly been used in small-scale projects focusing very often on hardware aspects. We want to take single-board computer architectures a step further by showing how to deploy part of an orchestration platform (OpenStack Swift) on a Raspberry Pi cluster to make it a useful platform for more sophisticated data collection and analysis applications located at the edge of a cloud. Our results illustrate that this is indeed possible, but that there are still shortcomings in terms of performance. Nevertheless, with the next generation of small single-board computers that have been introduced recently, we believe that this is a viable approach for certain application domains, such as private clouds or edge computing in harsh environments.en_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofseriesLecture Notes in Artificial Intelligence;
dc.rights
dc.titleA Platform for Edge Computing Based on Raspberry Pi Clustersen_US
dc.typeBook chapteren_US
dc.date.updated2018-05-15T12:53:02Z
dc.publication.titleData Analytics: 31st British International Conference on Databases, BICOD 2017, London, UK, July 10-12, 2017, Proceedings
dc.language.isiEN-GB
dc.description.fulltextreserveden_US


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