Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/22977
Title: Operational management of data centers energy efficiency by dynamic optimization - Based on a vector autoregressive model - Reinforcement learning (VAR-RL) approach
Authors: Hai, Y
Nguyen, TV
Choi, Y
Bahja, M
Lee, H
Keywords: DC;energy consumption;optimisation
Issue Date: 17-Feb-2021
Publisher: Collaborative European Research Conference
Citation: Hai, Y., Nguyen, T.V., Choi, Y., Bahja, M. and Lee, H. (2021) 'Operational management of data centers energy efficiency by dynamic optimization - Based on a vector autoregressive model - Reinforcement learning (VAR-RL) approach', CEUR Workshop Proceedings, 2020, 2815 pp. 371 - 381.
Abstract: With the increasing demands of digital computing, Data Centers (DCs) have become a leading scheme for global energy issues. Major efforts that can be observed for DC energy efficacy solutions are focusing on relatively problematic infrastructure designs. Nevertheless, we emphasised the managerial strategies of using the existing facilities to achieve energy efficiency through active intervention. It is believed that there exists a trade-off between the cooling devices and IT devices. Accordingly, the Vector Autoregressive Model- Reinforcement Learning(VAR-RL) approach will be proposed as a combination of traditional multivariate time series modeling technique and the artificial intelligence technique which allows us to predict and adjust the prediction of an error would help to explore the complex dynamic interrelationships between the two types of devices. Moreover, an optimization decision support system will also be conducted subsequently to optimize Power Usage Effectiveness (PUE) by controlling the combination of Air Conditioners (ACs). The proposed VAR-RL approach would not only increase the forecasting accuracy but also would adapt to the environment changes dynamically, this would give a better foundation for the DC energy efficiency optimization. The data we adopted is the real-time data from a DC located in Turkey. Consequently, the novel of this study would save the DC energy consumption tremendously.
Description: Proceedings of the 6th Collaborative European Research Conference (CERC 2020). Belfast, Northern-Ireland, UK, September 10-11, 2020, paper 24. Available at: http://ceur-ws.org/Vol-2815/.
URI: https://bura.brunel.ac.uk/handle/2438/22977
Appears in Collections:Brunel Business School Research Papers

Files in This Item:
File Description SizeFormat 
FullText.pdf700.41 kBAdobe PDFView/Open


This item is licensed under a Creative Commons License Creative Commons