ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2020, Vol. 57 ›› Issue (4): 671-687.doi: 10.7544/issn1000-1239.2020.20190866

Special Issue: 2020数据驱动网络专题

Previous Articles     Next Articles

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).

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)

CLC Number: