Evidence-aware Mobile Computational Offloading

dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorFlores, Huberen_US
dc.contributor.authorHui, Panen_US
dc.contributor.authorNurmi, Petterien_US
dc.contributor.authorLagerspetz, Eemilen_US
dc.contributor.authorTarkoma, Sasuen_US
dc.contributor.authorManner, Jukkaen_US
dc.contributor.authorKostakos, Vassilisen_US
dc.contributor.authorLi, Yongen_US
dc.contributor.authorSu, Xiangen_US
dc.contributor.departmentDepartment of Communications and Networkingen
dc.contributor.groupauthorInternet technologiesen
dc.contributor.organizationUniversity of Helsinkien_US
dc.contributor.organizationHong Kong University of Science and Technologyen_US
dc.contributor.organizationUniversity of Melbourneen_US
dc.contributor.organizationTsinghua Universityen_US
dc.contributor.organizationUniversity of Ouluen_US
dc.date.accessioned2018-04-04T09:37:10Z
dc.date.available2018-04-04T09:37:10Z
dc.date.issued2018-08en_US
dc.description.abstractComputational offloading can improve user experience of mobile apps through improved responsiveness and reduced energy footprint. Currently, offloading decisions are predominantly based on profiling performed on individual devices. While significant gains have been shown in benchmarks, these gains rarely translate to real-world use due to the complexity of contexts and parameters that affect offloading. We contribute by proposing crowdsensed evidence traces as a novel mechanism for improving the performance of offloading systems. Instead of limiting to profiling individual devices, crowdsensing enables characterising execution contexts across a community of users, providing better generalisation and coverage of contexts. We demonstrate the feasibility of using crowdsensing to characterize offloading contexts through an analysis of two crowdsensing datasets. Motivated by our results, we present the design and development of EMCO toolkit and platform as a novel solution for computational offloading. Experiments carried out on a testbed deployment in Amazon EC2 Ireland demonstrate that EMCO can consistently accelerate app execution while at the same time reduce energy footprint. We demonstrate that EMCO provides better scalability than current cloud platforms, being able to serve a larger number of clients without variations in performance. Our framework, use cases, and tools are available as open source from github.en
dc.description.versionPeer revieweden
dc.format.extent18
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationFlores, H, Hui, P, Nurmi, P, Lagerspetz, E, Tarkoma, S, Manner, J, Kostakos, V, Li, Y & Su, X 2018, ' Evidence-aware Mobile Computational Offloading ', IEEE Transactions on Mobile Computing, vol. 17, no. 8, pp. 1834-1850 . https://doi.org/10.1109/TMC.2017.2777491en
dc.identifier.doi10.1109/TMC.2017.2777491en_US
dc.identifier.issn1536-1233
dc.identifier.issn1558-0660
dc.identifier.otherPURE UUID: 559d6d7d-4576-4e0e-aa2b-79d391567944en_US
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/559d6d7d-4576-4e0e-aa2b-79d391567944en_US
dc.identifier.otherPURE LINK: http://www.scopus.com/inward/record.url?scp=85035756339&partnerID=8YFLogxKen_US
dc.identifier.otherPURE FILEURL: https://research.aalto.fi/files/18235018/manner_nbnfi_fe201803063765.pdfen_US
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/30574
dc.identifier.urnURN:NBN:fi:aalto-201804042037
dc.language.isoenen
dc.relation.ispartofseriesIEEE Transactions on Mobile Computingen
dc.rightsopenAccessen
dc.subject.keywordAccelerationen_US
dc.subject.keywordBig Dataen_US
dc.subject.keywordCloud computingen_US
dc.subject.keywordComputational Offloadingen_US
dc.subject.keywordContexten_US
dc.subject.keywordCrowdsensingen_US
dc.subject.keywordMobile applicationsen_US
dc.subject.keywordMobile Cloud Computingen_US
dc.subject.keywordMobile communicationen_US
dc.subject.keywordMobile computingen_US
dc.subject.keywordPerformance evaluationen_US
dc.titleEvidence-aware Mobile Computational Offloadingen
dc.typeA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessäfi
dc.type.versionacceptedVersion
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