000087681 001__ 87681
000087681 005__ 20200716101421.0
000087681 0247_ $$2doi$$a10.1016/j.future.2019.01.025
000087681 0248_ $$2sideral$$a111158
000087681 037__ $$aART-2019-111158
000087681 041__ $$aeng
000087681 100__ $$0(orcid)0000-0001-5549-7649$$aFabra, J.$$uUniversidad de Zaragoza
000087681 245__ $$aReducing the price of resource provisioning using EC2 spot instances with prediction models
000087681 260__ $$c2019
000087681 5060_ $$aAccess copy available to the general public$$fUnrestricted
000087681 5203_ $$aThe increasing demand of computing resources has boosted the use of cloud computing providers. This has raised a new dimension in which the connections between resource usage and costs have to be considered from an organizational perspective. As a part of its EC2 service, Amazon introduced spot instances (SI) as a cheap public infrastructure, but at the price of not ensuring reliability of the service. On the Amazon SI model, hired instances can be abruptly terminated by the service provider when necessary. The interface for managing SI is based on a bidding strategy that depends on non-public Amazon pricing strategies, which makes complicated for users to apply any scheduling or resource provisioning strategy based on such (cheaper) resources. Although it is believed that the use of the EC2 SIs infrastructure can reduce costs for final users, a deep review of literature concludes that their characteristics and possibilities have not yet been deeply explored. In this work we present a framework for the analysis of the EC2 SIs infrastructure that uses the price history of such resources in order to classify the SI availability zones and then generate price prediction models adapted to each class. The proposed models are validated through a formal experimentation process. As a result, these models are applied to generate resource provisioning plans that get the optimal price when using the SI infrastructure in a real scenario. Finally, the recent changes that Amazon has introduced in the SI model and how this work can adapt to these changes is discussed.
000087681 536__ $$9info:eu-repo/grantAgreement/ES/DGA/T21-17R-DISCO$$9info:eu-repo/grantAgreement/ES/MINECO/TIN2017-84796-C2-2-R$$9info:eu-repo/grantAgreement/ES/UZ/JIUZ-2018-TEC-04
000087681 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
000087681 590__ $$a6.125$$b2019
000087681 592__ $$a1.216$$b2019
000087681 591__ $$aCOMPUTER SCIENCE, THEORY & METHODS$$b8 / 108 = 0.074$$c2019$$dQ1$$eT1
000087681 593__ $$aComputer Networks and Communications$$c2019$$dQ1
000087681 593__ $$aSoftware$$c2019$$dQ1
000087681 593__ $$aHardware and Architecture$$c2019$$dQ1
000087681 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000087681 700__ $$0(orcid)0000-0002-9622-8186$$aEzpeleta, J.$$uUniversidad de Zaragoza
000087681 700__ $$0(orcid)0000-0002-6584-7259$$aÁlvarez, P.$$uUniversidad de Zaragoza
000087681 7102_ $$15007$$2570$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Lenguajes y Sistemas Inf.
000087681 773__ $$g96 (2019), 348-367$$pFuture gener. comput. syst.$$tFuture Generation Computer Systems-The International Journal of Grid Computing Theory Methods and Applications$$x0167-739X
000087681 8564_ $$s1314956$$uhttps://zaguan.unizar.es/record/87681/files/texto_completo.pdf$$yPostprint
000087681 8564_ $$s31929$$uhttps://zaguan.unizar.es/record/87681/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint
000087681 909CO $$ooai:zaguan.unizar.es:87681$$particulos$$pdriver
000087681 951__ $$a2020-07-16-08:40:58
000087681 980__ $$aARTICLE