TY - JOUR
AU - Austregésilo, Márcio Sergio Soares
AU - Callou, Gustavo
PY - 2019/08/03
Y2 - 2022/08/17
TI - Stochastic Models for Optimizing Availability, Cost and Sustainability of Data Center Power Architectures through Genetic Algorithm
JF - Revista de Informática Teórica e Aplicada
JA - RITA
VL - 26
IS - 2
SE - Regular Papers
DO - 10.22456/2175-2745.83498
UR - https://www.seer.ufrgs.br/index.php/rita/article/view/RITA_VOL26_NR2_27
SP - 27-44
AB - <div class="page" title="Page 1"><div class="section"><div class="layoutArea"><div class="column"><p><span>In recent years, the growth of information technology has required higher reliability, accessibility, collaboration, availability, and a reduction of costs on data centers due to factors such as social network, cloud computing, and e-commerce. These systems require redundant mechanisms on the data center infrastrucutre to achieve high availability, which may increase the electric energy consumption, impacting in both the sustainability and cost. This work proposes a multi-objective optimization approach, based on Genetic Algorithms, to optimize cost, sustainability and availability of data center power infrastructures. The main goal is to maximize availability and minimize cost and exergy consumed (adopted to estimate the environmental impacts). In order to compute such metrics, this work adopts the energy flow model (EFM), reliability block diagrams (RBD) and stochastic petri nets (SPN). Two case studies are conducted to show the applicability of the proposed strategy: (i) takes into account 5 typical data center architectures that were optimized to conduct the validation process of the proposed strategy; (ii) uses the optimization strategy in two architectures classified by ANSI / TIA-942 (TIER I and II). In both case studies, significant improvements were achieved in the results, which were very close to the optimum one that was obtained by a brute force algorithm that analyzes all the possibilities and returns the optimal solution. It is worth mentioning that the time used to obtain the results using the genetic algorithm approach was significantly lower (6,763,260 times), in comparison with the strategy which combines all the possible combinations to obtain the optimal result.</span></p></div></div></div></div>
ER -