Learning-based network path planning for traffic engineering
作者: Yuan ZuoYulei WuGeyong MinLaizhong Cui
作者单位: 1College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, EX4 4QF, UK
2College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, PR China
刊名: Future Generation Computer Systems, 2019, Vol.92 , pp.59-67
来源数据库: Elsevier Journal
DOI: 10.1016/j.future.2018.09.043
关键词: Traffic engineeringPath planningDeep learningSequence-to-sequence
原始语种摘要: Abstract(#br)Recent advances in traffic engineering offer a series of techniques to address the network problems due to the explosive growth of Internet traffic. In traffic engineering, dynamic path planning is essential for prevalent applications, e.g., load balancing, traffic monitoring and firewall. Application-specific methods can indeed improve the network performance but can hardly be extended to general scenarios. Meanwhile, massive data generated in the current Internet has not been fully exploited, which may convey much valuable knowledge and information to facilitate traffic engineering. In this paper, we propose a learning-based network path planning method under forwarding constraints for finer-grained and effective traffic engineering. We form the path planning problem as the...
全文获取路径: Elsevier  (合作)
影响因子:1.864 (2012)

  • traffic 话务量
  • planning 计划
  • engineering 工程
  • network 网络
  • learning 学识
  • sequence 次序
  • forwarding 转送
  • essential 本质的
  • leverage 杠杆率
  • adapt 适用