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dc.contributor.authorZhao, J
dc.contributor.authorTiplea, T
dc.contributor.authorMortier, Richard
dc.contributor.authorCrowcroft, Jonathon
dc.contributor.authorWang, L
dc.date.accessioned2018-11-14T00:31:46Z
dc.date.available2018-11-14T00:31:46Z
dc.date.issued2018-08-07
dc.identifier.isbn9781450359047
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/285062
dc.description.abstract© 2018 Copyright held by the owner/author(s). Data analytics on edge devices has gained rapid growth in research, industry, and different aspects of our daily life. This topic still faces many challenges such as limited computation resource on edge devices. In this paper, we further identify two main challenges: the composition and deployment of data analytics services on edge devices. We present the Zoo system to address these two challenge: on one hand, it provides simple and concise domain-specific language to enable easy and and type-safe composition of different data analytics services; on the other, it utilises multiple deployment backends, including Docker container, JavaScript, and MirageOS, to accommodate the heterogeneous edge deployment environment. We show the expressiveness of Zoo with a use case, and thoroughly compare the performance of different deployment backends in evaluation.
dc.publisherACM
dc.titleData analytics service composition and deployment on edge devices
dc.typeConference Object
prism.endingPage32
prism.publicationDate2018
prism.publicationNameBig-DAMA 2018 - Proceedings of the 2018 Workshop on Big Data Analytics and Machine Learning for Data Communication Networks, Part of SIGCOMM 2018
prism.startingPage27
dc.identifier.doi10.17863/CAM.32432
dcterms.dateAccepted2018-08-01
rioxxterms.versionofrecord10.1145/3229607.3229615
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2018-08-07
dc.contributor.orcidMortier, Richard [0000-0001-5205-5992]
dc.contributor.orcidCrowcroft, Jonathon [0000-0002-7013-0121]
dc.publisher.urlhttps://doi.org/10.1145/3229607
rioxxterms.typeConference Paper/Proceeding/Abstract
pubs.funder-project-idEPSRC (via University of Warwick) (RESWM34910001 CONTRIVE)
pubs.funder-project-idEPSRC (via Queen Mary University of London (QMUL)) (ECSA1W3R)
pubs.funder-project-idAlan Turing Institute (unknown)
cam.issuedOnline2018-08-07
pubs.conference-nameSIGCOMM '18: ACM SIGCOMM 2018 Conference
rioxxterms.freetoread.startdate2019-12-25


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