000126561 001__ 126561
000126561 005__ 20241125101152.0
000126561 0247_ $$2doi$$a10.1007/s12351-023-00773-x
000126561 0248_ $$2sideral$$a134010
000126561 037__ $$aART-2023-134010
000126561 041__ $$aeng
000126561 100__ $$aBrotcorne, Luce
000126561 245__ $$aA biobjective model for resource provisioning in multi-cloud environments with capacity constraints
000126561 260__ $$c2023
000126561 5060_ $$aAccess copy available to the general public$$fUnrestricted
000126561 5203_ $$aPrivate 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.
000126561 536__ $$9info:eu-repo/grantAgreement/ES/DGA/E41-20R$$9info:eu-repo/grantAgreement/ES/DGA/T21-23R$$9info:eu-repo/grantAgreement/ES/MICINN/PID2019-104263RB-C43
000126561 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000126561 590__ $$a2.3$$b2023
000126561 592__ $$a0.654$$b2023
000126561 591__ $$aOPERATIONS RESEARCH & MANAGEMENT SCIENCE$$b42 / 106 = 0.396$$c2023$$dQ2$$eT2
000126561 593__ $$aComputational Theory and Mathematics$$c2023$$dQ2
000126561 593__ $$aManagement of Technology and Innovation$$c2023$$dQ2
000126561 593__ $$aManagement Science and Operations Research$$c2023$$dQ2
000126561 593__ $$aStrategy and Management$$c2023$$dQ2
000126561 593__ $$aNumerical Analysis$$c2023$$dQ2
000126561 593__ $$aStatistics, Probability and Uncertainty$$c2023$$dQ2
000126561 593__ $$aModeling and Simulation$$c2023$$dQ2
000126561 594__ $$a5.7$$b2023
000126561 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000126561 700__ $$0(orcid)0000-0002-9622-8186$$aEzpeleta, Joaquín$$uUniversidad de Zaragoza
000126561 700__ $$0(orcid)0000-0002-5630-3719$$aGalé, Carmen$$uUniversidad de Zaragoza
000126561 7102_ $$12007$$2265$$aUniversidad de Zaragoza$$bDpto. Métodos Estadísticos$$cÁrea Estadís. Investig. Opera.
000126561 7102_ $$15007$$2570$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Lenguajes y Sistemas Inf.
000126561 773__ $$g23, 2 (2023), 31 [32 pp.]$$pOper. res. (Berl.)$$tOperational research (Berlin)$$x1109-2858
000126561 8564_ $$s2203919$$uhttps://zaguan.unizar.es/record/126561/files/texto_completo.pdf$$yVersión publicada
000126561 8564_ $$s1348180$$uhttps://zaguan.unizar.es/record/126561/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000126561 909CO $$ooai:zaguan.unizar.es:126561$$particulos$$pdriver
000126561 951__ $$a2024-11-22-12:07:31
000126561 980__ $$aARTICLE