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Energy efficiency in cloud computing based on mixture power spectral density prediction

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Abstract

Due to the budget and environmental issues, adaptive energy efficiency receives a lot of attention these days, especially for cloud computing. In the previous research, we developed a combined methodology based on nonparametric prediction and convex optimization to produce proactive energy efficiency-oriented solution. In this work, the predictive analysis was further enhanced by deriving the mixture power spectral density to model the complex cloud monitoring statistics. By engaging the improved technique to the predictive analysis, the prediction process was more adaptive to handle the fluctuation in system utilization. As a consequence, the optimization process could subsequently produce more appropriate setting for energy savings. After the infrastructure setting has been made available, the instruction of virtual machine migration was created and implemented by the cloud orchestrator. This instruction condensed the services into the pool of active facilities, satisfying the objective of power efficiency. Eventually, any physical machine out of the power configuration would be gradually terminated. Compared to our former method, the effectiveness of the proposed technique has been proven by cutting down 4.92% of energy consumption, while still maintaining a similar quality of services.

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Acknowledgements

This work was supported by Institute for Information and Communications Technology Planning and Evaluation (IITP) Grant and funded by the Korea government (MSIT) (No. 2017-0-00294, Service Mobility Support Distributed Cloud Technology). This work was also supported by the Social Policy Grant and funded by the Nazarbayev University.

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Authors and Affiliations

  1. Computer Science Department, Nazarbayev University, Nursultan, Kazakhstan

    Dinh-Mao Bui & Nguyen Anh Tu

  2. Department of Computer Science and Engineering, Kyung Hee University, Yongin, Korea

    Eui-Nam Huh

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  1. Dinh-Mao Bui
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  2. Nguyen Anh Tu
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  3. Eui-Nam Huh
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Correspondence to Dinh-Mao Bui.

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Bui, DM., Tu, N.A. & Huh, EN. Energy efficiency in cloud computing based on mixture power spectral density prediction. J Supercomput 77, 2998–3023 (2021). https://doi.org/10.1007/s11227-020-03380-1

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