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The exponential growth in Internet-connected devices, cloud services, and multimedia applications has placed unprecedented demands on network infrastructure. Traditional traffic engineering approaches, which rely on static rules and manual configuration, often struggle to adapt to such dynamic and complex environments. The need for networks that can intelligently and autonomously manage the flow of data has never been greater.

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Reinforcement Learning for Autonomous Traffic Engineering

  • 章节

  • 发表日期：

    2026年

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  • Het Mehta

摘要

The exponential growth in Internet-connected devices, cloud services, and multimedia applications has placed unprecedented demands on network infrastructure. Traditional traffic engineering approaches, which rely on static rules and manual configuration, often struggle to adapt to such dynamic and complex environments. The need for networks that can intelligently and autonomously manage the flow of data has never been greater.