Show simple item record

Files in this item


Item metadata

dc.contributor.authorLin, Yuhui
dc.contributor.authorBarker, Adam David
dc.contributor.authorThomson, John Donald
dc.identifier.citationLin , Y , Barker , A D & Thomson , J D 2020 , Modelling VM latent characteristics and predicting application performance using semi-supervised non-negative matrix factorization . in 2020 IEEE 13th International Conference on Cloud Computing (CLOUD) . IEEE Computer Society , IEEE 13th International Conference on Cloud Computing (CLOUD 2020) , 19/10/20 .
dc.identifier.otherPURE: 269586119
dc.identifier.otherPURE UUID: e5658e7c-f1c1-4c74-8571-78e72ca3772d
dc.identifier.otherScopus: 85099403085
dc.identifier.otherWOS: 000668554300061
dc.descriptionFunding: This work is a part of the ABC (Adaptive Brokerage for the Cloudproject) funded by EPSRC EP/R010528/1.en
dc.description.abstractSelecting a suitable VM instance type for an application can be difficult task because of the number of options and the variety of application requirements. Recent research takes a data-driven approach to model VM performance, but this requires carefully choosing a small set of relevant benchmarks as input. We propose a semi-supervised matrix-factorization-based latent variable approach to predict the performance of an unknown new application. This method allows to take a large set of benchmarks as input for VM performance modelling, and it uses the model and the performance measure of the new application on some of the target VMs to predict the performance on the rest of all VMs. We ran experiments with 373 micro-benchmarks from stress-ng and 37 AWS EC2 VMs to predict the scores of Geekbench accurately. Our initial results showed that the RMSE and STD of the predicted scores are 6.7 and 4.5 when sampling Geekbench on 5 VMs, and 10.0 and 2.8 when sampling 10.
dc.publisherIEEE Computer Society
dc.relation.ispartof2020 IEEE 13th International Conference on Cloud Computing (CLOUD)en
dc.rightsCopyright © 2020 the Author(s)/the owners. This work has been made available online in accordance with publisher policies or with permission. Permission for further reuse of this content should be sought from the publisher or the rights holder. This is the author created accepted manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at
dc.subjectCloud computingen
dc.subjectMatrix factorizationen
dc.subjectLatent variable modelen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.titleModelling VM latent characteristics and predicting application performance using semi-supervised non-negative matrix factorizationen
dc.typeConference itemen
dc.contributor.institutionUniversity of St Andrews.School of Computer Scienceen

This item appears in the following Collection(s)

Show simple item record