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Performance Prediction and Fine-Grained Resource Provision of Virtual Machines via LightGBM

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Data Mining and Big Data (DMBD 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1453))

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Abstract

It is significant to accurately predict the performance of virtual machines (VMs), and then provide the corresponding fine-grained resources according to users’ requirements for both users and cloud resource providers in IaaS cloud computing. In this paper, based on the idea of LightGBM, we first analyze the hardware/software, configuration and then runtime environmental features that may have impacts on the VM performance, and then propose a VM performance prediction model with Gradient-based One-side Sampling (GOSS) method, called VPGB. VPGB pays more attentions on the data instances that with the larger gradients so as to speed up the model training process and then predicts the VM performance accurately. In addition, based on the prediction results, we apply the genetic algorithm to find the optimal fine-grained resources configuration and then provide for users. Experimental results show that VPGB-based method can predict the VM performance accurately and provide the fine-grained VM resources for users effectively.

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Notes

  1. 1.

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Acknowledgement

This work is supported by National Natural Science Foundation of China (No. 61862068), Yunnan Expert Workstation of Xiaochun Cao, and Scientific Technology Innovation Team of Educational Big Data Application Technology in University of Yunnan Province.

Author information

Authors and Affiliations

  1. Key Laboratory of Education Informatization for Nationalities, Ministry of Education, Yunnan Normal University, Kunming, 650500, China

    Jia Hao & Jun Wang

  2. Yunnan Key Laboratory of Smart Education, Yunnan Normal University, Kunming, 650500, China

    Jia Hao & Jun Wang

  3. School of Information, Dehong Teachers’ College, Mangshi, 678400, China

    ZhaoXiang OuYang

Authors
  1. Jia Hao
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  2. Jun Wang
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  3. ZhaoXiang OuYang
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Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

  3. The University of Sydney, Sydney, Australia

    Albert Zomaya

  4. Guangzhou University, Guangzhou, China

    Hongyang Yan

  5. Guangdong Polytechnic Normal University, Guangzhou, China

    Jun Cai

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Hao, J., Wang, J., OuYang, Z. (2021). Performance Prediction and Fine-Grained Resource Provision of Virtual Machines via LightGBM. In: Tan, Y., Shi, Y., Zomaya, A., Yan, H., Cai, J. (eds) Data Mining and Big Data. DMBD 2021. Communications in Computer and Information Science, vol 1453. Springer, Singapore. https://doi.org/10.1007/978-981-16-7476-1_24

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  • DOI: https://doi.org/10.1007/978-981-16-7476-1_24

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-7475-4

  • Online ISBN: 978-981-16-7476-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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