Abstract
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.
Highlights
Social awareness has influenced the way the world works and how people live
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]
This paper considers stochastic Petri nets for conducting dependability analysis of data center power architectures
Summary
Social awareness has influenced the way the world works and how people live. Widely available. 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]. 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. We propose this new algorithm that uses two criteria of different classes that complement each other.
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