Optimizing the Cloud Data Center Availability Empowered by Surrogate Models

dc.contributor.author Gonçalves, Glauco
dc.contributor.author Gomes, Demis
dc.contributor.author Santos, Guto Leoni
dc.contributor.author Rosendo, Daniel
dc.contributor.author Moreira, André
dc.contributor.author Kelner, Judith
dc.contributor.author Sadok, Djamel
dc.contributor.author Endo, Patricia Takako
dc.date.accessioned 2020-01-04T07:28:18Z
dc.date.available 2020-01-04T07:28:18Z
dc.date.issued 2020-01-07
dc.description.abstract Making data centers highly available remains a challenge that must be considered since the design phase. The problem is selecting the right strategies and components for achieving this goal given a limited investment. Furthermore, data center designers currently lack reliable specialized tools to accomplish this task. In this paper, we disclose a formal method that chooses the components and strategies that optimize the availability of a data center while considering a given budget as a constraint. For that, we make use of stochastic models to represent a cloud data center infrastructure based on the TIA-942 standard. In order to improve the computational cost incurred to solve this optimization problem, we employ surrogate models to handle the complexity of the stochastic models. In this work, we use a Gaussian process to produce a surrogate model for a cloud data center infrastructure and we use three derivative-free optimization algorithms to explore the search space and to find optimal solutions. From the results, we observe that the Differential Evolution (DE) algorithm outperforms the other tested algorithms, since it achieves higher availability with a fair usage of the budget.
dc.format.extent 10 pages
dc.identifier.doi 10.24251/HICSS.2020.193
dc.identifier.isbn 978-0-9981331-3-3
dc.identifier.uri http://hdl.handle.net/10125/63932
dc.language.iso eng
dc.relation.ispartof Proceedings of the 53rd Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Service Analytics
dc.subject availability
dc.subject data center
dc.subject optimization
dc.subject stochastic models
dc.subject surrogate models
dc.title Optimizing the Cloud Data Center Availability Empowered by Surrogate Models
dc.type Conference Paper
dc.type.dcmi Text
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