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2020 | OriginalPaper | Buchkapitel

Energy-Efficient Virtual Machines Dynamic Integration for Robotics

verfasst von : Haoyu Wen, Sheng Zhou, Zie Wang, Ranran Wang, Jianmin Lu

Erschienen in: 2nd EAI International Conference on Robotic Sensor Networks

Verlag: Springer International Publishing

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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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Metadaten
Titel
Energy-Efficient Virtual Machines Dynamic Integration for Robotics
verfasst von
Haoyu Wen
Sheng Zhou
Zie Wang
Ranran Wang
Jianmin Lu
Copyright-Jahr
2020
Buch
2nd EAI International Conference on Robotic Sensor Networks

Print ISBN: 978-3-030-17762-1

Electronic ISBN: 978-3-030-17763-8

Copyright-Jahr: 2020

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

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