126561 20241125101152.0 doi 10.1007/s12351-023-00773-x sideral 134010 ART-2023-134010 eng Brotcorne, Luce A biobjective model for resource provisioning in multi-cloud environments with capacity constraints 2023 Access copy available to the general public Unrestricted Private and public clouds are good means for getting on-demand intensive computing resources. In such a context, selecting the most appropriate clouds and virtual machines (VMs) is a complex task. From the user’s point of view, the challenge consists in efficiently managing cloud resources while integrating prices and performance criteria. This paper focuses on the problem of selecting the appropriate clouds and VMs to run bags-of-tasks (BoT): big sets of identical and independent tasks. More precisely, we define new mathematical optimization models to deal with the time of use of each VMs and to jointly integrate the execution makespan and the cost into the objective function through a bi-objective problem. In order to provide trade-off solutions to the problem, we propose a lexicographic approach. In addition, we introduce, in two different ways, capacity constraints or bounds on the number of VMs available in the clouds. A global limit on the number of VMs or resource constraints at each time period can be defined. Computational experiments are performed on a synthetic dataset. Sensitivity analysis highlights the effect of the resource limits on the minimum makespan, the effect of the deadline in the total operation cost, the impact of considering instantaneous capacity constraints instead of a global limit and the trade-off between the cost and the execution makespan. info:eu-repo/grantAgreement/ES/DGA/E41-20R info:eu-repo/grantAgreement/ES/DGA/T21-23R info:eu-repo/grantAgreement/ES/MICINN/PID2019-104263RB-C43 info:eu-repo/semantics/openAccess by http://creativecommons.org/licenses/by/3.0/es/ 2.3 2023 OPERATIONS RESEARCH & MANAGEMENT SCIENCE 42 / 106 = 0.396 2023 Q2 T2 0.654 2023 Computational Theory and Mathematics 2023 Q2 Management of Technology and Innovation 2023 Q2 Management Science and Operations Research 2023 Q2 Strategy and Management 2023 Q2 Numerical Analysis 2023 Q2 Statistics, Probability and Uncertainty 2023 Q2 Modeling and Simulation 2023 Q2 5.7 2023 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Ezpeleta, Joaquín Universidad de Zaragoza (orcid)0000-0002-9622-8186 Galé, Carmen Universidad de Zaragoza (orcid)0000-0002-5630-3719 2007 265 Universidad de Zaragoza Dpto. Métodos Estadísticos Área Estadís. Investig. Opera. 5007 570 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Lenguajes y Sistemas Inf. 23, 2 (2023), 31 [32 pp.] Oper. res. (Berl.) Operational research (Berlin) 1109-2858 2203919 http://zaguan.unizar.es/record/126561/files/texto_completo.pdf Versión publicada 1348180 http://zaguan.unizar.es/record/126561/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:126561 articulos driver 2024-11-22-12:07:31 ARTICLE