Artificial Intelligence Platform for Mobile Service Computing


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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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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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).

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  • Artificial intelligence
  • Mobile service computing
  • Hadoop
  • Slurm
  • Schedule
  • TensorFlow
  • Caffe