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Research on application classification method in cloud computing environment

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Abstract

Energy consumption is a very important issue that has attracted the attention of many cloud providers as it takes a large quotient of the operation cost for cloud data center. To decrease the energy consumption in cloud data center, one possible solution is to process different types of applications with different strategies. To reach this goal, it is important to know the type of application before it be dealt with. In this paper, we present an application type classification method by monitoring the usage of the resources of application. Through analysis, we find that only part of the parameters are much related to different types of applications. Using these parameters we put forward a feature model that can effectively classify the types of different applications. Extensive experiments show that the model put forward can effectively and accurately classify CPU intensive application, I/O intensive application and network intensive application. It can be used as the basis of efficient utilization of the cloud resources.

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Acknowledgments

The authors are grateful for the good suggestions and comments from the reviewers without which the paper has no chance to reach its readers.

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Authors and Affiliations

  1. School of Computer Engineering and Science, Shanghai University, No. 333 Nanchen Rd., Shanghai, 200444, China

    Junjie Peng, Jinbao Chen & Xiaofei Zhi

  2. Department of Computer Science, Pace University, New York, USA

    Meikang Qiu

  3. College of Information Science and Engineering, Guilin University of Technology, Guilin, China

    Xiaolan Xie

Authors
  1. Junjie Peng
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  2. Jinbao Chen
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  3. Xiaofei Zhi
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  4. Meikang Qiu
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  5. Xiaolan Xie
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Corresponding author

Correspondence to Junjie Peng.

Additional information

This work is supported by National Science Foundation of China (No. 61103054, 61572305 and 61540054).

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Peng, J., Chen, J., Zhi, X. et al. Research on application classification method in cloud computing environment. J Supercomput 73, 3488–3507 (2017). https://doi.org/10.1007/s11227-016-1663-5

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  • DOI: https://doi.org/10.1007/s11227-016-1663-5

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