The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Ce document présente des modèles d'optimisation robustes pour minimiser la capacité de sauvegarde requise tout en fournissant une protection probabiliste contre plusieurs pannes simultanées de machines physiques sous des capacités de machines virtuelles incertaines dans un fournisseur de cloud. En cas de pannes aléatoires, les capacités requises pour les machines virtuelles sont allouées aux machines physiques de sauvegarde dédiées, qui sont déterminées à l'avance. Nous considérons deux incertitudes : l'événement de défaillance et la capacité de la machine virtuelle. En adoptant une technique d'optimisation robuste, nous formulons six problèmes de programmation linéaire en nombres entiers mixtes. Les résultats numériques montrent que pour un problème de petite taille, nos modèles présentés sont applicables au cas où les capacités des machines virtuelles sont incertaines, et en utilisant ces modèles, nous pouvons obtenir la solution optimale d'allocation des machines virtuelles sous l'incertitude. Une heuristique de recuit simulé est présentée pour résoudre des problèmes de grande taille. En utilisant cette heuristique, une solution approchée est obtenue pour un problème de grande taille.
Mitsuki ITO
Kyoto University
Fujun HE
Kyoto University
Eiji OKI
Kyoto University
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Mitsuki ITO, Fujun HE, Eiji OKI, "Backup Resource Allocation of Virtual Machines for Probabilistic Protection under Capacity Uncertainty" in IEICE TRANSACTIONS on Communications,
vol. E105-B, no. 7, pp. 814-832, July 2022, doi: 10.1587/transcom.2021EBP3144.
Abstract: This paper presents robust optimization models for minimizing the required backup capacity while providing probabilistic protection against multiple simultaneous failures of physical machines under uncertain virtual machine capacities in a cloud provider. If random failures occur, the required capacities for virtual machines are allocated to the dedicated backup physical machines, which are determined in advance. We consider two uncertainties: failure event and virtual machine capacity. By adopting a robust optimization technique, we formulate six mixed integer linear programming problems. Numerical results show that for a small size problem, our presented models are applicable to the case that virtual machine capacities are uncertain, and by using these models, we can obtain the optimal solution of the allocation of virtual machines under the uncertainty. A simulated annealing heuristic is presented to solve large size problems. By using this heuristic, an approximate solution is obtained for a large size problem.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2021EBP3144/_p
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@ARTICLE{e105-b_7_814,
author={Mitsuki ITO, Fujun HE, Eiji OKI, },
journal={IEICE TRANSACTIONS on Communications},
title={Backup Resource Allocation of Virtual Machines for Probabilistic Protection under Capacity Uncertainty},
year={2022},
volume={E105-B},
number={7},
pages={814-832},
abstract={This paper presents robust optimization models for minimizing the required backup capacity while providing probabilistic protection against multiple simultaneous failures of physical machines under uncertain virtual machine capacities in a cloud provider. If random failures occur, the required capacities for virtual machines are allocated to the dedicated backup physical machines, which are determined in advance. We consider two uncertainties: failure event and virtual machine capacity. By adopting a robust optimization technique, we formulate six mixed integer linear programming problems. Numerical results show that for a small size problem, our presented models are applicable to the case that virtual machine capacities are uncertain, and by using these models, we can obtain the optimal solution of the allocation of virtual machines under the uncertainty. A simulated annealing heuristic is presented to solve large size problems. By using this heuristic, an approximate solution is obtained for a large size problem.},
keywords={},
doi={10.1587/transcom.2021EBP3144},
ISSN={1745-1345},
month={July},}
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TY - JOUR
TI - Backup Resource Allocation of Virtual Machines for Probabilistic Protection under Capacity Uncertainty
T2 - IEICE TRANSACTIONS on Communications
SP - 814
EP - 832
AU - Mitsuki ITO
AU - Fujun HE
AU - Eiji OKI
PY - 2022
DO - 10.1587/transcom.2021EBP3144
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E105-B
IS - 7
JA - IEICE TRANSACTIONS on Communications
Y1 - July 2022
AB - This paper presents robust optimization models for minimizing the required backup capacity while providing probabilistic protection against multiple simultaneous failures of physical machines under uncertain virtual machine capacities in a cloud provider. If random failures occur, the required capacities for virtual machines are allocated to the dedicated backup physical machines, which are determined in advance. We consider two uncertainties: failure event and virtual machine capacity. By adopting a robust optimization technique, we formulate six mixed integer linear programming problems. Numerical results show that for a small size problem, our presented models are applicable to the case that virtual machine capacities are uncertain, and by using these models, we can obtain the optimal solution of the allocation of virtual machines under the uncertainty. A simulated annealing heuristic is presented to solve large size problems. By using this heuristic, an approximate solution is obtained for a large size problem.
ER -