Int J Performability Eng ›› 2019, Vol. 15 ›› Issue (11): 2962-2971.

### An Energy Prediction Model for Cloud Data Centers Through Performance Counter

Sa Menga,*, Peng Suna, Jie Luob,c, and Han Xua

1. aSchool of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China;
bSchool of Computer Science and Engineering, Beihang University, Beijing, 100191, China;
cState Key Laboratory of Software Development Environment, Beihang University, Beijing, 100191, China
• Submitted on  ;  Revised on  ; Accepted on
• Contact: * E-mail address: summerincuit@gmail.com
• About author:Sa Meng is currently pursuing a Ph.D. at the School of Computer Science and Engineering, University of Electronic Science and Technology of China (UESTC). Her current research interests include cloud computing, reliability modeling and optimization, and energy-efficient computing.Peng Sun is an Assistant Professor at the School of Computer Science and Engineering, University of Electronic Science and Technology of China (UESTC). His research interests include cloud computing, reliability modeling, and optimization.Han Xu is currently pursuing a Ph.D. at the School of Computer Science and Engineering, University of Electronic Science and Technology of China (UESTC). His research interests include system optimization of reliability, performance, power consumption and safety.

Abstract: In recent years, with an increased environmental protection concern, the carbon footprints of large-scale cloud data centers have come into public view. Energy efficiency has become a key indicator for these data centers. Management personnel in cloud data centers should know the relationship between the workload patterns and the energy consumption of the infrastructure in order to optimize energy efficiency. In this paper, we first discuss the energy consumption problems of cloud data centers and summarize the related work. Then, we purpose an energy prediction model to estimate the energy consumption of servers in cloud data centers based on performance counters of their processors. Afterward, the proposed model is tested and analyzed under a wide selection of benchmarks, including SPEC2006, I/Ozone, and Netperf. Finally, through analyzing the results of the contrast experiments, it is shown that the proposed energy prediction model can predict energy consumption of cloud servers with high accuracy.