@techreport{oh-nmrg-ai-adp-02,
    number =    {draft-oh-nmrg-ai-adp-02},
    type =      {Internet-Draft},
    institution =   {Internet Engineering Task Force},
    publisher = {Internet Engineering Task Force},
    note =      {Work in Progress},
    url =       {https://datatracker.ietf.org/doc/draft-oh-nmrg-ai-adp/02/},
    author =    {Oh Seokbeom and Yong-Geun Hong and Joo-Sang Youn and Hyunjeong Lee and Hyun-Kook Kahng},
    title =     {{AI-Based Distributed Processing Automation in Digital Twin Network}},
    pagetotal = 13,
    year =      2024,
    month =     jul,
    day =       8,
    abstract =  {This document discusses the use of AI technology and digital twin technology to automate the management of computer network resources distributed across different locations. Digital twin technology involves creating a virtual model of real-world physical objects or processes, which is utilized to analyze and optimize complex systems. In a digital twin network, AI-based network management by automating distributed processing involves utilizing deep learning algorithms to analyze network traffic, identify potential issues, and take proactive measures to prevent or mitigate those issues. Network administrators can efficiently manage and optimize their networks, thereby improving network performance and reliability. AI-based network management, utilizing digital twin network technology, also aids in optimizing network performance by identifying bottlenecks in the network and automatically adjusting network settings to enhance throughput and reduce latency. By implementing AI-based network management through automated distributed processing, organizations can improve network performance, and reduce the need for manual network management tasks.},
}