Name
Title | ||
|---|---|---|
EPC-TE: Explicit Path Control in Traffic Engineering with Deep Reinforcement Learning |
Abstract | ||
|---|---|---|
Segment Routing (SR) provides Traffic Engineering (TE) with Explicit Path Control (EPC) by steering data flows passing through a list of SR routers along a desired path. However, large-scale migration from a pure IP network to a full SR 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 EPC-TE to optimize TE performance in hybrid IP/SR networks where partially deployed SR routers coexist with legacy IP routers. We propose a concept of key nodes to achieve EPC over desired paths and a criterion to select which IP routers to upgrade first under a pre-defined upgrading ratio. EPC-TE leverages Deep Reinforcement Learning (DRL) to inference the optimal traffic splitting ratio across multiple controllable paths between source-destination pairs. EPC-TE can achieve comparable TE performance as a full SR network with an upgrading ratio less than 30%. Extensive experimental results with real-world topologies show that EPC-TE significantly outperforms other baseline TE solutions in minimizing maximum link utilization. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/GLOBECOM46510.2021.9685792 | 2021 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM) |
Keywords | DocType | ISSN |
Traffic Engineering, Segment Routing, Deep Reinforcement Learning | Conference | 2334-0983 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zeyu Luan | 1 | 0 | 0.68 |
| Lie Lu | 2 | 0 | 0.34 |
| Qing Li | 3 | 3222 | 433.87 |
| Jiang Yong | 4 | 156 | 41.60 |