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A Routing Optimization Method for Software-Defined Optical Transport Networks Based on Ensembles and Reinforcement

Junyan Chen1,2, Wei Xiao1, Xinmei Li1

  • 1School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China.

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|November 11, 2022
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Summary

This study introduces an ensembles- and message-passing neural-network-based Deep Q-Network (EMDQN) for optimizing optical transport networks (OTNs). EMDQN enhances routing, improving network throughput and link utilization with better generalization capabilities.

Keywords:
deep Q-networkensemble learningmessage-passing neural networkoptical transport networksoftware-defined networking

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Area of Science:

  • Computer Science
  • Telecommunications Engineering
  • Network Optimization

Background:

  • Optical transport networks (OTNs) are vital for backbone and metro transmission, requiring efficient routing and capacity maximization.
  • Current deep reinforcement learning (DRL) methods for OTN routing face challenges with sample efficiency, adaptability to network changes, and generalization.
  • Software-defined networking (SDN) offers potential for OTN optimization but requires robust DRL solutions.

Purpose of the Study:

  • To address the limitations of existing DRL routing methods in software-defined OTN scenarios.
  • To propose a novel DRL-based approach for optical network routing optimization.
  • To enhance the convergence, sample efficiency, and generalization capabilities of DRL agents in OTNs.

Main Methods:

  • Development of an ensembles- and message-passing neural-network-based Deep Q-Network (EMDQN).
  • Utilizing multiple EMDQN agents that select actions based on upper-confidence bounds for improved exploration.
  • Employing a message passing neural network (MPNN) within the DRL policy network to capture spatial network features.

Main Results:

  • The proposed EMDQN algorithm demonstrates superior convergence performance compared to existing methods.
  • EMDQN effectively enhances the throughput rate and link utilization of optical networks.
  • The EMDQN method exhibits improved generalization capabilities in optical network routing optimization.

Conclusions:

  • EMDQN provides a more effective solution for routing optimization in software-defined optical transport networks.
  • The approach overcomes key limitations of previous DRL methods, offering better adaptability and performance.
  • EMDQN represents a significant advancement in optimizing optical network capacity and efficiency.