ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2020, Vol. 57 ›› Issue (4): 671-687.doi: 10.7544/issn1000-1239.2020.20190866

所属专题: 2020数据驱动网络专题

• 网络技术 • 上一篇    下一篇

基于机器学习的智能路由算法综述

刘辰屹,徐明伟,耿男,张翔   

  1. (清华大学计算机科学与技术系 北京 100084) (liucheny19@mails.tsinghua.edu.cn)
  • 出版日期: 2020-04-01
  • 基金资助: 
    国家自然科学基金项目(61625203,61832013);国家重点研发计划项目(2017YFB0801701)

A Survey on Machine Learning Based Routing Algorithms

Liu Chenyi, Xu Mingwei, Geng Nan, Zhang Xiang   

  1. (Department of Computer Science and Technology, Tsinghua University, Beijing 100084)
  • Online: 2020-04-01
  • Supported by: 
    This work was supported by the National Natural Science Foundation of China (61625203, 61832013) and the National Key Research and Development Plan of China (2017YFB0801701).

摘要: 互联网的飞速发展催生了很多新型网络应用,其中包括实时多媒体流服务、远程云服务等.现有尽力而为的路由转发算法难以满足这些应用所带来的多样化的网络服务质量需求.随着近些年将机器学习方法应用于游戏、计算机视觉、自然语言处理获得了巨大的成功,很多人尝试基于机器学习方法去设计智能路由算法.相比于传统数学模型驱动的分布式路由算法而言,基于机器学习的路由算法通常是数据驱动的,这使得其能够适应动态变化的网络环境以及多样的性能评价指标优化需求.基于机器学习的数据驱动智能路由算法目前已经展示出了巨大的潜力,未来很有希望成为下一代互联网的重要组成部分.然而现有对于智能路由的研究仍然处于初步阶段.首先介绍了现有数据驱动智能路由算法的相关研究,展现了这些方法的核心思想和应用场景并分析了这些工作的优势与不足.分析表明,现有基于机器学习的智能路由算法研究主要针对算法原理,这些路由算法距离真实环境下部署仍然很遥远.因此接下来分析了不同的真实场景智能路由算法训练和部署方案并提出了2种合理的训练部署框架以使得智能路由算法能够低成本、高可靠性地在真实场景被部署.最后分析了基于机器学习的智能路由算法未来发展中所面临的机遇与挑战并给出了未来的研究方向.

关键词: 机器学习, 数据驱动路由算法, 深度学习, 强化学习, 服务质量

Abstract: The rapid development of the Internet accesses many new applications including real time multi-media service, remote cloud service, etc. These applications require various types of service quality, which is a significant challenge towards current best effort routing algorithms. Since the recent huge success in applying machine learning in game, computer vision and natural language processing, many people tries to design “smart” routing algorithms based on machine learning methods. In contrary with traditional model-based, decentralized routing algorithms (e.g.OSPF), machine learning based routing algorithms are usually data-driven, which can adapt to dynamically changing network environments and accommodate different service quality requirements. Data-driven routing algorithms based on machine learning approach have shown great potential in becoming an important part of the next generation network. However, researches on artificial intelligent routing are still on a very beginning stage. In this paper we firstly introduce current researches on data-driven routing algorithms based on machine learning approach, showing the main ideas, application scenarios and pros and cons of these different works. Our analysis shows that current researches are mainly for the principle of machine learning based routing algorithms but still far from deployment in real scenarios. So we then analyze different training and deploying methods for machine learning based routing algorithms in real scenarios and propose two reasonable approaches to train and deploy such routing algorithms with low overhead and high reliability. Finally, we discuss the opportunities and challenges and show several potential research directions for machine learning based routing algorithms in the future.

Key words: machine learning, data driven routing algorithm, deep learning, reinforcement learning, quality of service (QoS)

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