A Non-Intrusive, Traffic-Aware Prediction Framework for Power Consumption in Data Center Operations

Zheng Liu, Mian Zhang, Xusheng Zhang and Yun Li
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Zheng Liu: School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
Mian Zhang: School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
Xusheng Zhang: School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
Yun Li: School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China

Energies, 2020, vol. 13, issue 3, 1-19

Abstract: Modern cloud computing relies heavily on data centers, which usually host tens of thousands of servers. Predicting the power consumption accurately in data center operations is crucial for energy optimization. In this paper, we formulate the power consumption prediction at both the fine-grained and coarse-grained level. We carefully discuss the desired properties of an applicable prediction model and propose a non-intrusive, traffic-aware prediction framework for power consumption. We design a character-level encoding strategy for URIs and employ both convolutional and recurrent neural networks to develop a unified prediction model. We use real datasets to simulate requests and analyze the characteristics of the collected power consumption series. Extensive experiments demonstrate that our proposed framework can achieve superior prediction performance compared to other popular leading prediction methods.

Keywords: power consumption prediction; fine-grained prediction; coarse-grained prediction; non-intrusive features (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
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