用强化学习提升故障下伊森斯坦-雅可比网络的路由鲁棒性。
Deep Reinforcement Learning for Fault-Adaptive Routing in Eisenstein-Jacobi Interconnection Topologies
- 设计基于多目标奖励的强化学习路由策略，自适应绕过故障区域。
- 在9个故障节点下实现94%有效可达率，接近理论最优水平。
- 适合分布式部署，无需全局拓扑信息，性能优于传统方法。
随着多核架构密度增加，互连网络需兼具高性能与容错能力。伊森斯坦-雅可比（EJ）网络具有对称6正则拓扑，但故障条件下传统路由策略失效。本文评估三种路由范式：确定性贪婪自适应路由、理论最优的Dijkstra算法，以及基于强化学习（RL）的方法。通过惩罚靠近故障点、奖励路径效率的多目标奖励函数，RL代理学会绕开易导致死锁的簇状故障。Dijkstra算法在已知完整拓扑时计算全局最优路径，揭示了故障网络的真实连通极限。定量分析显示，贪婪路由在9个故障节点下有效可达率降至10%，而Dijkstra表明52-54%为理论上限。RL代理实现94%有效可达率和91%分组送达率，适合分布式部署。吞吐量测试表明，RL在全负载下保持超90%归一化吞吐量，且在拥塞下因隐式负载均衡优于Dijkstra。结果证明，基于强化学习的自适应策略在效率与最优性间取得平衡，实现无需全局拓扑知识的自愈通信。
原文摘要 · Abstract (English)
The increasing density of many-core architectures necessitates interconnection networks that are both high-performance and fault-resilient. Eisenstein-Jacobi (EJ) networks, with their symmetric 6-regular topology, offer superior topological properties but challenge traditional routing heuristics under fault conditions. This paper evaluates three routing paradigms in faulty EJ environments: deterministic Greedy Adaptive Routing, theoretically optimal Dijkstra's algorithm, and a reinforcement learning (RL)-based approach. Using a multi-objective reward function to penalize fault proximity and reward path efficiency, the RL agent learns to navigate around clustered failures that typically induce dead-ends in greedy geometric routing. Dijkstra's algorithm establishes the theoretical performance ceiling by computing globally optimal paths with complete topology knowledge, revealing the true connectivity limits of faulty networks. Quantitative analysis at nine faulty nodes shows greedy routing catastrophically degrades to 10% effective reachability and packet delivery, while Dijkstra proves 52-54% represents the topological optimum. The RL agent achieves 94% effective reachability and 91% packet delivery, making it suitable for distributed deployment. Furthermore, throughput evaluations demonstrate that RL sustains over 90% normalized throughput across all loads, actually outperforming Dijkstra under congestion through implicit load balancing strategies. These results establish RL-based adaptive policies as a practical solution that bridges the gap between greedy's efficiency and Dijkstra's optimality, providing robust, self-healing communication in fault-prone interconnection networks without requiring the global topology knowledge or computational overhead of optimal algorithms.
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