基于强化学习的可复用路由算法，提升SDN网络负载均衡与收敛速度。
Intelligent Routing Algorithm over SDN: Reusable Reinforcement Learning Approach
- 利用可复用强化学习，动态学习网络状态并避免路径环路。
- 相比传统方法，负载均衡性能更优，多流量请求时收敛更快。
- 支持源端细粒度路由，减少控制器与网络平面通信开销。
流量路由对互联网正常运行至关重要。随着用户和流量增长，研究者致力于开发能适应多种服务质量（QoS）需求的智能路由算法。基于强化学习（RL）的路由算法已展现出优于传统方法的性能。本文提出一种面向QoS、可复用的强化学习路由算法——RLSR-Routing，该算法在软件定义网络（SDN）环境下运行。学习过程中，算法确保无环路径探索；在为单个流量需求（源-目的对及指定流量量）寻找路径时，可学习整体网络的QoS状态，从而在后续处理其他流量需求时加速算法收敛。通过结合段路由（Segment Routing），该算法实现基于流的源端包路由，并减少控制器与网络平面间的通信开销。实验表明，该算法在负载均衡方面优于传统方法，且在处理多个流量需求时，收敛速度显著快于非可复用的强化学习方法。
原文摘要 · Abstract (English)
Traffic routing is vital for the proper functioning of the Internet. As users and network traffic increase, researchers try to develop adaptive and intelligent routing algorithms that can fulfill various QoS requirements. Reinforcement Learning (RL) based routing algorithms have shown better performance than traditional approaches. We developed a QoS-aware, reusable RL routing algorithm, RLSR-Routing over SDN. During the learning process, our algorithm ensures loop-free path exploration. While finding the path for one traffic demand (a source destination pair with certain amount of traffic), RLSR-Routing learns the overall network QoS status, which can be used to speed up algorithm convergence when finding the path for other traffic demands. By adapting Segment Routing, our algorithm can achieve flow-based, source packet routing, and reduce communications required between SDN controller and network plane. Our algorithm shows better performance in terms of load balancing than the traditional approaches. It also has faster convergence than the non-reusable RL approach when finding paths for multiple traffic demands.
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