A Deep Neural Network based Approach to Energy Efficiency Analysis for Cloud Data Center

• Published in: 2019 IEEE 17th International Conference on Industrial Informatics (INDIN) Vol. 1; pp. 1397 - 1404
• Main Authors: Ounifi, Hibat-Allah, Gherbi, Abdelouahed, Kara, Nadjia, Li, Wubin
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.07.2019
• Subjects: Data Center; Deep Neural Network; energy efficiency; Power Usage Effectiveness; Prediction
• ISSN: 2378-363X
• DOI: 10.1109/INDIN41052.2019.8972019

The energy consumption growth of the Information and Communication Technology (ICT) sector contributes to almost 2% of the global carbon footprint with an estimated trend of 3-3.6% by 2020. Most of this growth (45%) can be attributed to data centers (DC) which now represent the core infrastructure for different industries. Furthermore, cloud DCs are complex systems composed of several ICT and non-ICT (i.e. mechanical and electrical) sub-systems. The variety of configurations and the inter-dependencies of the different DC sub-systems leads to enormous challenges in understanding and optimizing DC energy efficiency based on the Power Usage Effectiveness (PUE) metric. Within this context, we focus in this work on analyzing the behavior of Deep Neural Network (DNN)-based model to predict the DC energy efficiency metric (PUE). In fact, the proposed model is used to evaluate the impact of various DC sub-systems on energy efficiency. Through an experimentation with real datasets from a real DC, we observed that DNN-based model achieves a good Root Mean Square Error (RMSE). The obtained results of this experimentation indicate that our proposed DNN-based model improves the PUE optimization, and consequently, shows its promise for a practical implementation.