Abstract
Resource prediction promotes dynamic scheduling and energy saving in cloud computing. However, resource prediction becomes a challenge with the diversity and dynamicity of the cloud environment. Existing methods merely focus on single specific resource and ignore the correlation among resources, resulting in inaccurate predictions. Therefore, we propose a trend-matching resources coupled prediction method (TMRCP) based on incremental learning over data stream, which consists of three algorithms. Firstly, to cope with the diversity of the cloud environment, we propose a Resources Utilization Trend Matching algorithm (RUTM), which defines a new similarity measure for multi-dimensional sequences and takes the correlation among resources into consideration. Secondly, we propose a dynamic prediction window adjustment algorithm that selects appropriate prediction length for different resource utilization trends to overcome the disadvantage of fixed window. Thirdly, in response to the sudden changes, we put forward a mixed synthesis algorithm to improve the robustness of the method. Experiments on Google’s cluster usage trace show that the Mean Absolute Percentage Error of TMRCP is 4.7%, 20% better than the state-of-the-art. In addition, the TMRCP is still accurate in multi-step-ahead prediction.
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Acknowledgments
This work was supported by the National Natural Science Foundation of China (Grant No. 61379052), the National Key Research and Development Program (Grant No. 2016YFB1000101), the Natural Science Foundation for Distinguished Young Scholars of Hunan Province (Grant No. 14JJ1026), Specialized Research Fund for the Doctoral Program of Higher Education (Grant No. 20124307110015), the National Natural Science Foundation of China (Grant No. 61502513), the National Natural Science Foundation of China (Grant No. 61502513).
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Wu, R., Wang, Y., Ma, X., Cheng, L. (2017). TMRCP: A Trend-Matching Resources Coupled Prediction Method over Data Stream. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10638. Springer, Cham. https://doi.org/10.1007/978-3-319-70139-4_51
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DOI: https://doi.org/10.1007/978-3-319-70139-4_51
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