Show simple item record

dc.contributor.authorBilal, Muhammad
dc.contributor.authorSerafini, Marco
dc.contributor.authorCanini, Marco
dc.contributor.authorRodrigues, Rodrigo
dc.date.accessioned2021-02-15T07:02:47Z
dc.date.available2021-02-15T07:02:47Z
dc.date.issued2020-08
dc.identifier.citationBilal, M., Serafini, M., Canini, M., & Rodrigues, R. (2020). Do the best cloud configurations grow on trees? Proceedings of the VLDB Endowment, 13(12), 2563–2575. doi:10.14778/3407790.3407845
dc.identifier.issn2150-8097
dc.identifier.doi10.14778/3407790.3407845
dc.identifier.urihttp://hdl.handle.net/10754/667431
dc.description.abstractCloud configuration optimization is the procedure to determine the number and the type of instances to use when deploying an application in cloud environments, given a cost or performance objective. In the absence of a performance model for the distributed application, black-box optimization can be used to perform automatic cloud configuration. Numerous black-box optimization algorithms have been developed; however, their comparative evaluation has so far been limited to the hyper-parameter optimization setting, which differs significantly from the cloud configuration problem. In this paper, we evaluate 8 commonly used black-box optimization algorithms to determine their applicability for the cloud configuration problem. Our evaluation, using 23 different workloads, shows that in several cases Bayesian optimization with Gradient boosted regression trees performs better than methods chosen by prior work.
dc.description.sponsorshipMuhammad Bilal was supported by a fellowship from the Erasmus Mundus Joint Doctorate in Distributed Computing (EMJD-DC) program funded by the European Commission (EACEA) (FPA2012-0030). Work done in part while author was interning at KAUST. This research was supported by Fundac ̧ ̃ao para a Ciˆen-cia e a Tecnologia (FCT), under projects UIDB/50021/2020 andCMUP-ERI/TIC/0046/2014.
dc.publisherVLDB Endowment
dc.relation.urlhttps://dl.acm.org/doi/10.14778/3407790.3407845
dc.rightsArchived with thanks to Proceedings of the VLDB Endowment. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. For any use beyond those covered by this license, obtain permission by emailinginfo@vldb.org. Copyright is held by the owner/author(s). Publication rights licensed to the VLDB Endowment.
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.titleDo the best cloud configurations grow on trees? An experimental evaluation of black box algorithms for optimizing cloud workloads
dc.typeArticle
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalProceedings of the VLDB Endowment
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionUCLouvain
dc.contributor.institutionUniversity of Massachusetts Amherst
dc.contributor.institutionIST(ULisboa)/INESC-ID
dc.identifier.volume13
dc.identifier.issue12
dc.identifier.pages2563-2575
kaust.personCanini, Marco
refterms.dateFOA2021-02-15T07:05:37Z


Files in this item

Thumbnail
Name:
3407790.3407845.pdf
Size:
1.153Mb
Format:
PDF
Description:
Published Version

This item appears in the following Collection(s)

Show simple item record

Archived with thanks to Proceedings of the VLDB Endowment. This  work  is  licensed  under  the  Creative  Commons  Attribution-NonCommercial-NoDerivatives 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. For any use beyond those covered by this license, obtain permission by emailinginfo@vldb.org. Copyright is held by the owner/author(s). Publication rights licensed to the VLDB Endowment.
Except where otherwise noted, this item's license is described as Archived with thanks to Proceedings of the VLDB Endowment. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. For any use beyond those covered by this license, obtain permission by emailinginfo@vldb.org. Copyright is held by the owner/author(s). Publication rights licensed to the VLDB Endowment.