An Algorithm to Optimize Electrical Flows

Ferreira, Joao; Callou, Gustavo; Dantas, Jamilson; Souza, Rafael Borim de; Maciel, Paulo

doi:10.1109/smc.2013.26

Cited by 4 publications

(5 citation statements)

References 22 publications

(22 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The algorithm proposed in this paper, named power load distribution algorithm-depth (PLDA-D), improves the results presented in our previous work when considering the operational cost and energy efficiency of data centers [8][9][10]. Now, we have obtained the shortest path, using Bellman algorithm instructions, considering the energy cost as the main metric and the maximum energy flow (Ford-Fulkerson), considering the energy efficiency of each component of the data center's electrical infrastructure.…”

Section: Introductionsupporting

confidence: 51%

“…The power load distribution algorithm-depth (PLDA-D) is proposed to minimize the electrical energy consumption of the system represented through EFM models [32]. PLDA-D is a depth search extension of PLDA [9,10]. The Bellman-Ford algorithm [33] is used for searches of the smaller path in a weighted digraph, whose edges have a weight, including a negative one.…”

Section: Power Load Distribution Algorithm In Depth Searchmentioning

confidence: 99%

See 1 more Smart Citation

PLDAD—An Algorihm to Reduce Data Center Energy Consumption

et al. 2018

Self Cite

View full text Add to dashboard Cite

Due to the demands of new technologies such as social networks, e-commerce and cloud computing, more energy is being consumed in order to store all the produced data. While these new technologies require high levels of availability, a reduction in the cost and environmental impact is also expected. The present paper proposes a power balancing algorithm (power load distribution algorithm-depth (PLDA-D)) to optimize the energy distribution of data center electrical infrastructures. The PLDA-D is based on the Bellman and Ford–Fulkerson flow algorithms that analyze energy-flow models (EFM). EFM computes the power efficiency, sustainability and cost metrics of data center infrastructures. To demonstrate the applicability of the proposed strategy, we present a case study that analyzes four power infrastructures. The results obtained show about a 3.8% reduction in sustainability impact and operational costs.

show abstract

Section: Introductionsupporting

confidence: 51%

Section: Power Load Distribution Algorithm In Depth Searchmentioning

confidence: 99%

PLDAD—An Algorihm to Reduce Data Center Energy Consumption

et al. 2018

Self Cite

View full text Add to dashboard Cite

show abstract

“…In this work, we integrated an ANN into the energy flow model. An EFM with artificial neural networks (ANN) could expand the horizons of the modeling strategy previously adopted in [8,10,27]. In addition to computing exergy consumption, operational costs, input power and PUE, this new approach enables the values of these metrics to be forecast, based on a historical series.…”

Section: Applying Anns To the Efmmentioning

confidence: 99%

An Artificial Neural Network Approach to Forecast the Environmental Impact of Data Centers

et al. 2019

Self Cite

View full text Add to dashboard Cite

Due to the high demands of new technologies such as social networks, e-commerce and cloud computing, more energy is being consumed in order to store all the data produced and provide the high availability required. Over the years, this increase in energy consumption has brought about a rise in both the environmental impacts and operational costs. Some companies have adopted the concept of a green data center, which is related to electricity consumption and CO2 emissions, according to the utility power source adopted. In Brazil, almost 70% of electrical power is derived from clean electricity generation, whereas in China 65% of generated electricity comes from coal. In addition, the value per kWh in the US is much lower than in other countries surveyed. In the present work, we conducted an integrated evaluation of costs and CO2 emissions of the electrical infrastructure in data centers, considering the different energy sources adopted by each country. We used a multi-layered artificial neural network, which could forecast consumption over the following months, based on the energy consumption history of the data center. All these features were supported by a tool, the applicability of which was demonstrated through a case study that computed the CO2 emissions and operational costs of a data center using the energy mix adopted in Brazil, China, Germany and the US. China presented the highest CO2 emissions, with 41,445 tons per year in 2014, followed by the US and Germany, with 37,177 and 35,883, respectively. Brazil, with 8459 tons, proved to be the cleanest. Additionally, this study also estimated the operational costs assuming that the same data center consumes energy as if it were in China, Germany and Brazil. China presented the highest kWh/year. Therefore, the best choice according to operational costs, considering the price of energy per kWh, is the US and the worst is China. Considering both operational costs and CO2 emissions, Brazil would be the best option.

show abstract

“…Este trabalho mede a exergia destruída. Quanto menor o valor desta métrica, menor o seu impacto em termos de sustentabilidade [17].…”

Section: Exergia E Sustentabilidadeunclassified

“…Ferreira et. al [17] propõem um algoritmo de distribuição de carga de energia (PLDA), baseado no algoritmo de Ford-Fulkerson, que minimiza o consumo de energia em um modelo proposto de fluxo de energia. A aplicabilidade do PLDA proposto foi verificada por um estudo de caso que analisa seis arquiteturas elétricas de nuvem privada observando resultados significativos, como a redução no consumo de energia de 10,7% e uma redução no impacto ambiental.…”

Section: Trabalhos Relacionadosunclassified

Stochastic Models for Optimizing Availability, Cost and Sustainability of Data Center Power Architectures through Genetic Algorithm

Austregésilo

Callou

2019

RITA

Self Cite

View full text Add to dashboard Cite

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. Resumo: Nos últimos anos, o crescimento da tecnologia da informação exigiu maior confiabilidade, acessibilidade, colaboração, disponibilidade e redução de custos nos data centers devido a fatores como redes sociais, computação em nuvem e comércio eletrônico. Esses sistemas exigem mecanismos redundantes na infraestrutura do data center para alcançar uma alta disponibilidade, o que pode aumentar o consumo de energia elétrica, impactando tanto na sustentabilidade quanto no custo. Este trabalho propõe uma abordagem de otimização multi-objetivo, baseada em Algoritmos Genéticos, para otimizar custos, sustentabilidade e disponibilidade de infraestruturas de energia em data centers. O principal objetivo é maximizar a disponibilidade e minimizar o custo e a exergia consumida (adotada para estimar os impactos ambientais). Para computar tais métricas, este trabalho adota o modelo de fluxo de energia (EFM), diagramas de blocos de confiabilidade (RBD) e redes de petri estocásticas (SPN). Dois estudos de caso são conduzidos: (i) leva em consideração 5 arquiteturas típicas de data centers para mostrar a aplicabilidade e validação da estratégia proposta; (ii) utiliza a estratégia de otimização em duas arquiteturas classificadas pela norma ANSI/TIA-942 (TIER I e II). Em ambos estudos de caso, observou-se uma melhora significativa nos resultados que fic...

show abstract

An Algorithm to Optimize Electrical Flows

Cited by 4 publications

References 22 publications

PLDAD—An Algorihm to Reduce Data Center Energy Consumption

PLDAD—An Algorihm to Reduce Data Center Energy Consumption

An Artificial Neural Network Approach to Forecast the Environmental Impact of Data Centers

Stochastic Models for Optimizing Availability, Cost and Sustainability of Data Center Power Architectures through Genetic Algorithm

Contact Info

Product

Resources

About