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Dynamic allocation strategy of VM resources with fuzzy transfer learning method

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Abstract

Predicting virtual machine (VM) workload to realize dynamic allocation resources has always been a hot issue in research, most of the current resource prediction methods are based on different load resources to build prediction models, it is difficult to realize knowledge transfer between multi-task prediction models to complete multiple tasks’ prediction. This paper proposes a innovative method - dynamic resource allocation method based on fuzzy migration learning, which is based on the feature attributes of command line processes to predict multiple resource loads of VMs and realize dynamic allocation of VM resources. Firstly, Principal Components Analysis (PCA) algorithm is used to reduce the attributes dimension of command line process. Then, we apply fuzzy transfer learning, which is based on fuzzy neural network with the capability to deal with the uncertainty in transfer learning, to predict multiple resource loads of VMs with the strong regularities of command line processes, and dynamically configure the resources of VMs. In the experimental procedure, we take CPU and memory as examples, on the basis of CPU prediction model, the model parameters are transferred to memory prediction model obtaining good results. The implementation verified the effectiveness of the proposed method, achieves the aim of dynamic resource allocation of VMs, and improved the VMs’ performance.

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Acknowledgments

This work was supported in part by the National Natural Science Foundation of China (Grant no. 61702098), the science and technology planning project of Guangdong Province, China (2014A010103039, 2015A010103022, 20 14B090901002), the Dongguan science and technology plan project, Guangdong Privince, China(2014509102211), the Natural science fund for colleges and universities of Jiangsu province, China (18KJB520049), the industry University-Research-Cooperation Project in Jiangsu Province, China (BY2018124), and the Next Generation Internet Technology Innovation Project of Cernet, China (NGII20170515). The author would like to thank the anonymous reviewers and the editors for their comments and suggestions that improved the quality of the manuscript. In addition, the project received the funding supporting from National Scientific Data Sharing Platform for Population and Health.

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Authors and Affiliations

  1. School of Medical Informatics, Xuzhou Medical University, Xuzhou, China

    Xiang Wu, Huanhuan Wang, Dashun Wei & Minyu Shi

  2. School of Computer Science and Technology, Dongguan University of Technology, Dongguan, 523808, China

    Wei Tan

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  1. Xiang Wu
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  2. Huanhuan Wang
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  3. Wei Tan
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  5. Minyu Shi
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Correspondence to Xiang Wu.

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Wu, X., Wang, H., Tan, W. et al. Dynamic allocation strategy of VM resources with fuzzy transfer learning method. Peer-to-Peer Netw. Appl. 13, 2201–2213 (2020). https://doi.org/10.1007/s12083-020-00885-7

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