SNS: Smart Node Selection for Scalable Traffic Engineering in Segment Routing Networks

doi:10.1109/TNSM.2024.3424928

	SNS: Smart Node Selection for Scalable Traffic Engineering in Segment Routing Networks
	Wang, Linghao 1,2; Lu, Lu 3; Wang, Miao 1,2,4; Li, Zhiqiang 3; Yang, Hongwei 3; Zhu, Shuyong 1,3; Zhang, Yujun 1,4,5
	2025-02-01
发表期刊	IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT
ISSN	1932-4537
卷号	22 期号:1 页码:92-106
摘要	Segment routing (SR) is an emerging architecture that can benefit traffic engineering (TE). Nowadays, TE in SR networks (SR-TE) is often solved as an optimization problem to optimize network performance such as link utilization. As network size grows rapidly, implementing SR-TE suffers from scalability issues, including long computation time, high control overhead and expensive deployment cost. In this paper, we propose Smart Node Selection (SNS), a scalable SR-TE method with learning-based node selection (NS). NS is a recently proposed technique for reducing computation time of SR-TE. It first selects a subset of nodes as candidate intermediate nodes to route traffic, then builds linear programming (LP) models that can be solved efficiently. However, existing NS methods use simple heuristics and consider only network topology, which may lead to unsatisfying network performance. To address this problem, we for the first time formulates NS as a reinforcement learning task, which learns a selection policy to achieve better trade-offs between TE performance and computation time, considering both topology and traffic. Besides, we extend NS with additional selection policies and a customized training algorithm, making it a unified framework for scalable SR-TE, which reduces not only computation time, but also control overhead and deployment cost. Performance evaluations on various real-world topologies and traffic matrices show that SNS significantly reduces computation time and control overhead of existing LP models while offering good network performance, and can also be used in partially deployed SR networks to reduce deployment cost.
关键词	Scalability Routing Costs Computational modeling Network topology Task analysis Topology Traffic engineering segment routing reinforcement learning routing optimization
DOI	10.1109/TNSM.2024.3424928
收录类别	SCI
语种	英语
资助项目	NSFC-Civil Aviation Joint Fund[U2333201] ; National Natural Science Foundation of China[62372429] ; Innovation Funding of ICT, CAS[E461040] ; Pilot for Major Scientific Research Facility of Jiangsu Province of China[BM2021800] ; Institute of Computing Technology, Chinese Academy of Sciences-China Mobile Communications Group Co., Ltd. Joint Institute
WOS研究方向	Computer Science
WOS类目	Computer Science, Information Systems
WOS记录号	WOS:001445070500024
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/40681
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Zhang, Yujun
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 101408, Peoples R China 3.China Mobile Res Inst, Dept Basic Network Technol, Beijing 10053, Peoples R China 4.Nanjing Inst InforSuperBahn, Nanjing 211167, Peoples R China 5.Jiangsu Future Networks Innovat Inst, Nanjing 211111, Peoples R China
推荐引用方式 GB/T 7714	Wang, Linghao,Lu, Lu,Wang, Miao,et al. SNS: Smart Node Selection for Scalable Traffic Engineering in Segment Routing Networks[J]. IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT,2025,22(1):92-106.
APA	Wang, Linghao.,Lu, Lu.,Wang, Miao.,Li, Zhiqiang.,Yang, Hongwei.,...&Zhang, Yujun.(2025).SNS: Smart Node Selection for Scalable Traffic Engineering in Segment Routing Networks.IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT,22(1),92-106.
MLA	Wang, Linghao,et al."SNS: Smart Node Selection for Scalable Traffic Engineering in Segment Routing Networks".IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 22.1(2025):92-106.