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Multi-step-ahead host load prediction using autoencoder and echo state networks in cloud computing

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Abstract

Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources that can be rapidly provisioned and released with minimal management effort or service provider interaction. There are many proposals for resource management approaches for cloud infrastructures, but effective resource management is still a major challenge for the leading cloud infrastructure operators (e.g., Amazon, Microsoft, Google), because the details of the underlying workloads and the real-world operational demands are too complex. Among those proposals, accurate host load prediction is one of the most effective measures to address this challenge. In this paper, we proposed a new method for host load prediction, which uses an autoencoder as the pre-recurrent feature layer of the echo state networks. The aim of our proposed method is to predict the host load in the future interval based on Google cluster usage dataset. Experiments performed on Google load traces show that our proposed method achieves higher accuracy than the state-of-the-art methods.

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Acknowledgments

This work was partially supported by Grant No. BE2011169, BK2011563 from the Natural Science Foundation of Jiangsu Province and Grant No. 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, Yu Zhou, Yao Yu, Jie Yuan & Sidan Du

  2. Harbin Engineering University, Harbin, China

    Xianglei Xing

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  1. Qiangpeng Yang
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  2. Yu Zhou
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  3. Yao Yu
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  4. Jie Yuan
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  5. Xianglei Xing
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  6. Sidan Du
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Correspondence to Qiangpeng Yang.

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Yang, Q., Zhou, Y., Yu, Y. et al. Multi-step-ahead host load prediction using autoencoder and echo state networks in cloud computing. J Supercomput 71, 3037–3053 (2015). https://doi.org/10.1007/s11227-015-1426-8

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  • DOI: https://doi.org/10.1007/s11227-015-1426-8

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