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An adaptive workload-aware power consumption measuring method for servers in cloud data centers

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Abstract

As cloud computing technologies and applications develop rapidly in recent years, the quantity and size of cloud datacenters have been ever-increasing, making the overconsumption of energy in datacenters become a widespread concern. To reduce the energy cost by servers, we must first build an accurate power model to achieve flexible, device-free power consumption measuring. However, most of the previous work related to server power modeling solely apply to the server and virtual machine levels, and the existing power models fail to take into account the heterogeneity in workload. Therefore, we first propose separate power consumption models based on the distinction of workload types including CPU-intensive, I/O-intensive, memory-intensive, and mixed workload. Then, we present an adaptive workload-aware power consumption measuring method (WSPM) for cloud servers. Our method proactively selects an appropriate power model for the upcoming workload through workload clustering, forecasting and classification, which are implemented using K-means, ARIMA, and threshold-based methods, respectively. We conducted several experiments to evaluate the performance of the key components of our method. The result shows: (1) the accuracy of our future workload forecasting on real traces of requests to our servers, (2) the accuracy of the power consumption measured by WSPM, and (3) the effectiveness of our workload-aware method in reducing real-time power estimation lag. Overall, the proposed method simplifies power modeling under diverse workloads without losing accuracy, making it a general and highly available solution for cloud data centers.

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Notes

  1. https://www.wattsupmeters.com/.

  2. https://github.com/jws7/CloudMonitor/.

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Acknowledgements

This work is supported by National Natural Science Foundation of China (Grant Nos. 61872084, 61772205), Key-Area Research and Development Program of Guangdong Province (No.2020B010164003), Guangzhou Science and Technology Program key projects (Grant Nos. 202007040002, 201902010040, and 201907010001), Guangzhou Development Zone Science and Technology (Grant No. 2018GH17), Guangdong Major Project of Basic and Applied Basic Research (2019B030302002), and the Fundamental Research Funds for the Central Universities, SCUT (Grant No. 2019ZD26).

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Authors and Affiliations

  1. School of Computer Science and Engineering, South China University of Technology, Guangzhou, Guangdong, China

    Weiwei Lin & Yufeng Zhang

  2. Department of Computer Science, University of Warwick, Coventry, CV4 7AL, United Kingdom

    Wentai Wu & Ligang He

  3. Department of Computer and Information Science, University of Macau, Taipa, Macau SAR, China

    Simon Fong

  4. Huawei Technologies Co., Ltd., Shenzhen, Guangdong, China

    Jia Chang

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  1. Weiwei Lin
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Correspondence to Weiwei Lin.

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Lin, W., Zhang, Y., Wu, W. et al. An adaptive workload-aware power consumption measuring method for servers in cloud data centers. Computing 105, 515–538 (2023). https://doi.org/10.1007/s00607-020-00819-4

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