IEICE Transactions on Communications
Online ISSN : 1745-1345
Print ISSN : 0916-8516
Regular Section
Robust Optimization Model for Primary and Backup Capacity Allocations against Multiple Physical Machine Failures under Uncertain Demands in Cloud
Mitsuki ITOFujun HEKento YOKOUCHIEiji OKI
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2023 Volume E106.B Issue 1 Pages 18-34

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Abstract

This paper proposes a robust optimization model for probabilistic protection under uncertain capacity demands to minimize the total required capacity against multiple simultaneous failures of physical machines. The proposed model determines both primary and backup virtual machine allocations simultaneously under the probabilistic protection guarantee. To express the uncertainty of capacity demands, we introduce an uncertainty set that considers the upper bound of the total demand and the upper and lower bounds of each demand. The robust optimization technique is applied to the optimization model to deal with two uncertainties: failure event and capacity demand. With this technique, the model is formulated as a mixed integer linear programming (MILP) problem. To solve larger sized problems, a simulated annealing (SA) heuristic is introduced. In SA, we obtain the capacity demands by solving maximum flow problems. Numerical results show that our proposed model reduces the total required capacity compared with the conventional model by determining both primary and backup virtual machine allocations simultaneously. We also compare the results of MILP, SA, and a baseline greedy algorithm. For a larger sized problem, we obtain approximate solutions in a practical time by using SA and the greedy algorithm.

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© 2023 The Institute of Electronics, Information and Communication Engineers
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