| 题名 | Poster: < SmartTE: Partially Deployed Segment Routing for Smart Traffic Engineering with Deep Reinforcement Learning > |
| 作者 | |
| 通讯作者 | Luan, Zeyu |
| DOI | |
| 发表日期 | 2021
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| 会议名称 | IFIP Networking Conference and Workshops (IFIP Networking)
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| 会议录名称 | |
| 会议日期 | JUN 21-24, 2021
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| 会议地点 | null,null,ELECTR NETWORK
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| 出版地 | 345 E 47TH ST, NEW YORK, NY 10017 USA
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| 出版者 | |
| 摘要 | Segment Routing (SR) provides Traffic Engineering (TE) with the ability of explicit path control by steering traffic passing through specific SR routers along a desired path. However, large-scale migration from a legacy IP network to a full SR-enabled one requires prohibitive hardware replacement and software update. Therefore, network operators prefer to upgrade a subset of IP routers into SR routers during a transitional period. This paper proposes SmartTE to optimize TE performance in hybrid IP/SR networks where partially deployed SR routers coexist with legacy IP routers. We use two centrality criteria in graph theory to decide which IP routers should be upgraded into SR routers under a given upgrading ratio. SmartTE leverages Deep Reinforcement Learning (DRL) to infer the optimal traffic splitting ratio across multiple pre-defined paths between source-destination pairs. Extensive experimental results with real-world topologies show that SmartTE outperforms other baseline TE solutions in minimizing the maximum link utilization and achieves comparable performance as a full SR network by upgrading only 30% IP routers. |
| 关键词 | |
| 学校署名 | 非南科大
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| 语种 | 英语
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| 相关链接 | [来源记录] |
| 收录类别 | |
| 资助项目 | National Key Research and Development Program of China[2018YFB1804704]
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| WOS研究方向 | Computer Science
; Telecommunications
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| WOS类目 | Computer Science, Hardware & Architecture
; Computer Science, Information Systems
; Computer Science, Theory & Methods
; Telecommunications
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| WOS记录号 | WOS:000853016800050
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| 来源库 | Web of Science
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| 引用统计 |
被引频次[WOS]：0
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| 成果类型 | 会议论文 |
| 条目标识符 | http://sustech.caswiz.com/handle/2SGJ60CL/402382 |
| 专题 | 南方科技大学 |
| 作者单位 | 1.Tsinghua Univ, Tsinghua Berkeley Shenzhen Inst, Shenzhen, Peoples R China 2.Southern Univ Sci & Technol, Shenzhen, Peoples R China 3.Tsinghua Univ, Shenzhen Int Grad Sch, Shenzhen, Peoples R China 4.Peng Cheng Lab, PCL Res Ctr Networks & Commun, Shenzhen, Peoples R China |
| 推荐引用方式 GB/T 7714 |
Luan, Zeyu,Li, Qing,Jiang, Yong. Poster: < SmartTE: Partially Deployed Segment Routing for Smart Traffic Engineering with Deep Reinforcement Learning >[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2021.
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| 条目包含的文件 | 条目无相关文件。 | |||||
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