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A new method based on PSR and EA-GMDH for host load prediction in cloud computing system

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Abstract

Host load prediction is one of the most effective measures for improving resource utilization in cloud computing systems. Due to the drastic fluctuation of the host load in the Cloud, accurately predicting the host load remains a challenge. In this paper, we propose a new prediction method that combines the Phase Space Reconstruction method and the Group Method of Data Handling based on an Evolutionary Algorithm. The performance of our proposed method is evaluated using two real-world load traces. The first is the load trace in a traditional distributed system, whereas the second is in a Google data center. The results show that the proposed method achieves a better prediction performance than some state-of-the-art methods.

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Acknowledgments

This work was partially supported by Grant Nos. BE2011169, BK2011563 from the Natural Science Foundation of Jiangsu Province and Grant Nos. 61100111, 61300157, 61201425, 61271231 from the Natural Science Foundation of China.

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Authors and Affiliations

  1. School of Electronic Science and Engineering, Nanjing University, Nanjing, China

    Qiangpeng Yang, Chenglei Peng, He Zhao, Yao Yu, Yu Zhou, Ziqiang Wang & Sidan Du

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  1. Qiangpeng Yang
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  2. Chenglei Peng
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  3. He Zhao
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  4. Yao Yu
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  5. Yu Zhou
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  6. Ziqiang Wang
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  7. Sidan Du
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Corresponding author

Correspondence to Qiangpeng Yang.

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Q. Yang and C. Peng are joint first authors.

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Yang, Q., Peng, C., Zhao, H. et al. A new method based on PSR and EA-GMDH for host load prediction in cloud computing system. J Supercomput 68, 1402–1417 (2014). https://doi.org/10.1007/s11227-014-1097-x

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