Operational management of data centers energy efficiency by dynamic optimization - Based on a vector autoregressive model - Reinforcement learning (VAR-RL) approach

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.