IEICE TRANSACTIONS on Communications
Backup Resource Allocation of Virtual Machines for Probabilistic Protection under Capacity Uncertainty
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E105-B No.7 pp.814-832
- Publication Date
- 2022/07/01
- Publicized
- 2022/01/17
- Online ISSN
- 1745-1345
- DOI
- 10.1587/transcom.2021EBP3144
- Type of Manuscript
- PAPER
- Category
- Network
Authors
Mitsuki ITO
Kyoto University
Fujun HE
Kyoto University
Eiji OKI
Kyoto University
Keyword
Latest Issue
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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},}
Copy
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 -
English
