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Artificial Intelligence Platform for Mobile Service Computing

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A Correction to this article was published on 19 November 2019

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Abstract

Since the birth of artificial intelligence, the theory and the technology have become more mature, and the application field is expanding. Mobile networks and applications have grown quickly in recent years, and mobile computing is the new computing paradigm for mobile networks. In this paper, we build an artificial intelligence platform for a mobile service, which supports deep learning frameworks such as TensorFlow and Caffe. We describe the overall architecture of the AI platform for a GPU cluster in mobile service computing. In the GPU cluster, based on the scheduling layer, we propose Yarn by the Slurm scheduler to not only improve the distributed TensorFlow plug-in for the Slurm scheduling layer but also to extend YARN to manage and schedule GPUs. The front-end of the high-performance AI platform has the attributes of availability, scalability and efficiency. Finally, we verify the convenience, scalability, and effectiveness of the AI platform by comparing the performance of single-chip and distributed versions for the TensorFlow, Caffe and YARN systems.

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Change history

  • 19 November 2019

    The Publisher regrets an error on the printed front cover of the October 2019 issue. The issue numbers were incorrectly listed as Volume 91, Nos. 10-12, October 2019. The correct number should be: "Volume 91, No. 10, October 2019"

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Acknowledgments

This work was partly supported by the National Key R&D Program of China (No. 2017YFB0202202), the Major Research Plan of National Natural Science Foundation of China (No. 91530324), the Super-computing Resource Pool of Chinese Academy of Sciences Information Project (No. XXH13503).

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Authors and Affiliations

  1. Computer Network Information Center, Chinese Academy of Sciences, Beijing, 100190, China

    Haikuo Zhang, Zhonghua Lu, Ke Xu, Fang Liu, Jue Wang, Yangang Wang & Rongqiang Cao

  2. University of Chinese Academy of Sciences, Beijing, 100049, China

    Haikuo Zhang, Zhonghua Lu & Ke Xu

  3. China Internet Network Information Center, Beijing, 100190, China

    Haikuo Zhang

  4. University of Illinois at Urbana-Champaign, Champaign, IL, 61820, USA

    Yuchen Pang

  5. State Grid Hebei Electric Power Company, Shijiazhuang, 050022, Hebei Province, China

    Liandong Chen

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  1. Haikuo Zhang
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  2. Zhonghua Lu
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  3. Ke Xu
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  9. Rongqiang Cao
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Correspondence to Haikuo Zhang.

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Zhang, H., Lu, Z., Xu, K. et al. Artificial Intelligence Platform for Mobile Service Computing. J Sign Process Syst 91, 1179–1189 (2019). https://doi.org/10.1007/s11265-019-1438-3

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