Multi-Objective Optimization Strategy Applied in Data Centers Electrical Subsystems

Data center infrastructures must have high availability, low cost, and high energy efficiency. However, these objectives are often conflicting. For instance, an additional Uninterruptible Power Supply (UPS) improves the system availability but may jeopardize the total cost and the energy consumption. This paper presents a strategy based on the Non-dominated Sorting Genetic Algorithm II (NSGA-II), a multi-objective evolutionary optimization algorithm, to improve the design of electrical data center architectures. To show the applicability of the proposed strategy, we present a comparative study between the brute force algorithm and proposed strategy based on NSGA-II, as it has shown promising results in multi-objective problems 1, 2 and 3. When setting complex electrical infrastructure data center models, the results showed that applying the proposed strategy reduces the runtime 961 times and achieves Pareto optimal curvature with a difference of approximately 1%.