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Energy-Efficient Virtual Machines Dynamic Integration for Robotics

  • Conference paper
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2nd EAI International Conference on Robotic Sensor Networks

Part of the book series: EAI/Springer Innovations in Communication and Computing ((EAISICC))

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Abstract

The rapid development of cloud computing technology has brought a lot of energy consumption. However, the utilization rate of resources such as data center CPUs is often less than half. Therefore, if the virtual machines in operation are centrally integrated into some servers, and idle servers are switched to low-power modes, the power consumption of data centers can be greatly reduced. The consumption. The traditional research on the integration of virtual machines is mainly based on the current load of the host to set a high-load threshold or periodically perform the migration. At present, research based on time-series prediction faces the problem of low prediction accuracy. In order to solve these problems, this paper synthetically considers the influence of multi-order Markov model and CPU state at different times, and proposes a new K-order mixed Markov model for CPU load prediction of the host for a period of time in the future. By conducting large-scale data experiments on the CloudSim simulation platform, the host load forecasting method proposed in this paper is compared with traditional load detection methods, and the proposed model is greatly reduced in the number of virtual machine migrations and data center energy consumption. And the violation of the SLA is also at an acceptable level.

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Authors and Affiliations

  1. School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan, China

    Haoyu Wen, Sheng Zhou, Zie Wang, Ranran Wang & Jianmin Lu

Authors
  1. Haoyu Wen
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  2. Sheng Zhou
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  3. Zie Wang
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  4. Ranran Wang
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  5. Jianmin Lu
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Corresponding author

Correspondence to Haoyu Wen .

Editor information

Editors and Affiliations

  1. Department of Mechanical and Control Engineering, Kyushu Institute of Technology, Kitakyushu, Japan

    Huimin Lu

  2. Department of Electronics Engineering and Computer Science, Fukuoka University, Fukuoka, Japan

    Li Yujie

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Wen, H., Zhou, S., Wang, Z., Wang, R., Lu, J. (2020). Energy-Efficient Virtual Machines Dynamic Integration for Robotics. In: Lu, H., Yujie, L. (eds) 2nd EAI International Conference on Robotic Sensor Networks. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-17763-8_9

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  • DOI: https://doi.org/10.1007/978-3-030-17763-8_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-17762-1

  • Online ISBN: 978-3-030-17763-8

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