Optimizing the Cloud Data Center Availability Empowered by Surrogate Models

Gonçalves, Glauco
Gomes, Demis
Santos, Guto Leoni
Rosendo, Daniel
Moreira, André
Kelner, Judith
Sadok, Djamel
Endo, Patricia Takako
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Making data centers highly available remains a challenge that must be considered since the design phase. The problem is selecting the right strategies and components for achieving this goal given a limited investment. Furthermore, data center designers currently lack reliable specialized tools to accomplish this task. In this paper, we disclose a formal method that chooses the components and strategies that optimize the availability of a data center while considering a given budget as a constraint. For that, we make use of stochastic models to represent a cloud data center infrastructure based on the TIA-942 standard. In order to improve the computational cost incurred to solve this optimization problem, we employ surrogate models to handle the complexity of the stochastic models. In this work, we use a Gaussian process to produce a surrogate model for a cloud data center infrastructure and we use three derivative-free optimization algorithms to explore the search space and to find optimal solutions. From the results, we observe that the Differential Evolution (DE) algorithm outperforms the other tested algorithms, since it achieves higher availability with a fair usage of the budget.
Service Analytics, availability, data center, optimization, stochastic models, surrogate models
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