用图注意力网络提升光路复用下的路由波长分配效率
Reinforcement Learning with Graph Attention for Routing and Wavelength Assignment with Lightpath Reuse
- 用图注意力网络建模网络拓扑，优化路由与波长分配决策
- 相比之前最优强化学习方法，吞吐量提升2.5%（17.4 Tbps）
- 适合研究光网络资源调度或强化学习应用的工程师与学者
现有研究多关注灵活网格网络中的路由与频谱分配，但实际生产系统普遍采用固定网格搭配灵活速率波长转换器。本文针对这一场景下的光路复用路由与波长分配（RWA-LR）问题进行重新审视，系统性地评估启发式算法，发现按跳数而非总长度排序候选路径可使吞吐率提高6%。采用图注意力网络构建策略与价值函数的强化学习代理，在真实网络结构上实现更优决策。我们公开全部代码，并在实验中超越此前最优强化学习方法2.5%（平均额外吞吐17.4 Tbps），优于最佳启发式算法1.2%（平均额外吞吐8.5 Tbps）。该微小增益反映出长时序资源分配任务中学习高效策略的挑战性。
原文摘要 · Abstract (English)
Many works have investigated reinforcement learning (RL) for routing and spectrum assignment on flex-grid networks but only one work to date has examined RL for fixed-grid with flex-rate transponders, despite production systems using this paradigm. Flex-rate transponders allow existing lightpaths to accommodate new services, a task we term routing and wavelength assignment with lightpath reuse (RWA-LR). We re-examine this problem and present a thorough benchmarking of heuristic algorithms for RWA-LR, which are shown to have 6% increased throughput when candidate paths are ordered by number of hops, rather than total length. We train an RL agent for RWA-LR with graph attention networks for the policy and value functions to exploit the graph-structured data. We provide details of our methodology and open source all of our code for reproduction. We outperform the previous state-of-the-art RL approach by 2.5% (17.4 Tbps mean additional throughput) and the best heuristic by 1.2% (8.5 Tbps mean additional throughput). This marginal gain highlights the difficulty in learning effective RL policies on long horizon resource allocation tasks.
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