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dc.contributor.authorBoonprasop, Chalee
dc.contributor.authorBarker, Adam David
dc.date.accessioned2019-02-27T15:30:08Z
dc.date.available2019-02-27T15:30:08Z
dc.date.issued2018-04-23
dc.identifier257949722
dc.identifier9ece74d8-6ad9-4366-a25e-990139c37821
dc.identifier85049667180
dc.identifier.citationBoonprasop , C & Barker , A D 2018 , Simplified cloud instance selection . in Proceedings of the 5th Workshop on CrossCloud Infrastructures & Platforms (CrossCloud'18) . , 5 , ACM , New York, NY , 5th Workshop on CrossCloud Infrastructures & Platforms , Porto , Portugal , 23/04/18 . https://doi.org/10.1145/3195870.3195875en
dc.identifier.citationworkshopen
dc.identifier.isbn9781450356534
dc.identifier.urihttps://hdl.handle.net/10023/17165
dc.description.abstractCloud computing delivers computational services to anyone over the internet. The cloud providers offer these services through a simplified billing model where customers can rent services based on the types of computing power they require. However, given the vast choice, it is complicated for a user to select the optimal instance types for a given workload or application. In this paper, we propose a user-friendly cloud instance recommendation system, which given a set of weighted coefficients representing the relevance of CPU, memory, storage and network along with a price, will recommend the best performing instances. The system only requires provider specified data about instance types and doesn’t require costly cloud benchmarking. We evaluate our approach on Microsoft Azure across a number of different common workload types.
dc.format.extent6
dc.format.extent203954
dc.language.isoeng
dc.publisherACM
dc.relation.ispartofProceedings of the 5th Workshop on CrossCloud Infrastructures & Platforms (CrossCloud'18)en
dc.subjectCloud computingen
dc.subjectCloud brokeren
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectT Technologyen
dc.subject3rd-NDASen
dc.subject.lccQA75en
dc.subject.lccTen
dc.titleSimplified cloud instance selectionen
dc.typeConference itemen
dc.contributor.sponsorEPSRCen
dc.contributor.institutionUniversity of St Andrews. School of Computer Scienceen
dc.identifier.doi10.1145/3195870.3195875
dc.identifier.grantnumberEP/R010528/1en


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