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Electronics and Telecommunications Research Institute (한국전자통신연구원)
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A machine learning assisted optical multistage interconnection network: Performance analysis and hardware demonstration

  • Sangeetha Rengachary Gopalan (School of Electronics Engineering, Vellore Institute of Technology) ;
  • Hemanth Chandran (School of Electronics Engineering, Vellore Institute of Technology) ;
  • Nithin Vijayan (School of Electronics Engineering, Vellore Institute of Technology) ;
  • Vikas Yadav (School of Electronics Engineering, Vellore Institute of Technology) ;
  • Shivam Mishra (School of Electronics Engineering, Vellore Institute of Technology)
  • Received : 2021.07.27
  • Accepted : 2022.09.21
  • Published : 2023.02.20
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Abstract

Integration of the machine learning (ML) technique in all-optical networks can enhance the effectiveness of resource utilization, quality of service assurances, and scalability in optical networks. All-optical multistage interconnection networks (MINs) are implicitly designed to withstand the increasing highvolume traffic demands at data centers. However, the contention resolution mechanism in MINs becomes a bottleneck in handling such data traffic. In this paper, a select list of ML algorithms replaces the traditional electronic signal processing methods used to resolve contention in MIN. The suitability of these algorithms in improving the performance of the entire network is assessed in terms of injection rate, average latency, and latency distribution. Our findings showed that the ML module is recommended for improving the performance of the network. The improved performance and traffic grooming capabilities of the module are also validated by using a hardware testbed.

Keywords

Acknowledgement

The corresponding author would like to record her deep appreciation and thanks to the Department of Science and Technology, Government of India, for the financial grant under the Young Scientist Scheme (YSS/2015/000986).

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