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Gaussian process for predicting CPU utilization and its application to energy efficiency

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Abstract

For the past ten years, Gaussian process has become increasingly popular for modeling numerous inferences and reasoning solutions due to the robustness and dynamic features. Particularly concerning regression and classification data, the combination of Gaussian process and Bayesian learning is considered to be one of the most appropriate supervised learning approaches in terms of accuracy and tractability. However, due to the high complexity in computation and data storage, Gaussian process performs poorly when processing large input dataset. Because of the limitation, this method is ill-equipped to deal with the large-scale system that requires reasonable precision and fast reaction rate. To improve the drawback, our research focuses on a comprehensive analysis of Gaussian process performance issues, highlighting ways to drastically reduce the complexity of hyper-parameter learning and training phases, which could be applicable in predicting the CPU utilization in the demonstrated application. In fact, the purpose of this application is to save the energy by distributively engaging the Gaussian process regression to monitor and predict the status of each computing node. Subsequently, a migration mechanism is applied to migrate the system-level processes between multi-core and turn off the idle one in order to reduce the power consumption while still maintaining the overall performance.

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Acknowledgments

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) NRF-2014R1A2A2A01003914.

This work was supported by the IT R&D program of MSIP/IITP. [14-000-09-001, Development of General-Purpose OS and Virtualization Technology to Reduce 30 % of Energy for High-density Servers based on Low-power Processors].

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Authors and Affiliations

  1. Department of Computer Engineering, Kyung Hee University, Suwon, Republic of Korea

    Dinh-Mao Bui, Huu-Quoc Nguyen, Muhammad Bilal Amin & Sungyoung Lee

  2. Department of Multimedia Science, SookMyung Women’s University, Seoul, Republic of Korea

    YongIk Yoon

  3. HPC System Research Section/Cloud Computing Department, ETRI, Daejeon, Republic of Korea

    SungIk Jun

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  1. Dinh-Mao Bui
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  2. Huu-Quoc Nguyen
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Correspondence to Dinh-Mao Bui.

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Bui, DM., Nguyen, HQ., Yoon, Y. et al. Gaussian process for predicting CPU utilization and its application to energy efficiency. Appl Intell 43, 874–891 (2015). https://doi.org/10.1007/s10489-015-0688-4

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