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Host load prediction with long short-term memory in cloud computing

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Abstract

Host load prediction is significant for improving resource allocation and utilization in cloud computing. Due to the higher variance than that in a grid, accurate prediction remains a challenge in the cloud system. In this paper, we apply a concise yet adaptive and powerful model called long short-term memory to predict the mean load over consecutive future time intervals and actual load multi-step-ahead. Two real-world load traces were used to evaluate the performance. One is the load trace in the Google data center, and the other is that in a traditional distributed system. The experiment results show that our proposed method achieves state-of-the-art performance with higher accuracy in both datasets.

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Acknowledgements

This work was partially supported by Grant No. BE2015152 from the Natural Science Foundation of Jiangsu Province and Grant Nos. 61100111, 61300157, 61201425, 61271231 from the National Natural Science Foundation of China.

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Authors and Affiliations

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

    Binbin Song, Yao Yu, Yu Zhou, Ziqiang Wang & Sidan Du

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  1. Binbin Song
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  2. Yao Yu
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  3. Yu Zhou
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  4. Ziqiang Wang
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  5. Sidan Du
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Correspondence to Yao Yu.

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Song, B., Yu, Y., Zhou, Y. et al. Host load prediction with long short-term memory in cloud computing. J Supercomput 74, 6554–6568 (2018). https://doi.org/10.1007/s11227-017-2044-4

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