TANGCO：学习应对过载驱动级联故障的拓扑感知容量分配

中文摘要 AI 辅助

从电网到交通网络再到云集群的网络化系统，通过容量有限的节点承载负载。负载超过容量的节点会发生故障，并将其负载转移到邻居节点，这可能触发全系统的级联故障。我们研究如何在节点间分配固定容量预算，以在局部负载再分配下抵御这类级联故障。该问题颇具挑战性，因为尚无已知的最优分配方案，且“故障或存活”的目标函数不可微且为分段常数，因此精确优化方法与基于梯度的优化方法无法直接应用。我们提出TANGCO（Topology-Aware Neural Graph-Guided Capacity Optimization，拓扑感知神经图引导的容量优化），该模型采用图神经网络策略，通过级联模拟器结合策略梯度学习与启发式锚点进行训练。我们在5个人工图族以及涵盖电力、道路、航空和互联网拓扑的5个真实网络上对TANGCO进行评估。在全部450个人工实例中，该学习策略均优于4种手动设计的启发式方法中的最优者；在45种真实网络场景中的40种场景下，其鲁棒性提升幅度介于1.6%至246%之间。该学习策略可在同一图族内的未见图上迁移，也可在相关拓扑间部分迁移；在人工图上预训练的TANGCO^pre，在未见真实网络上的表现与针对每个网络单独训练的结果相当。其训练规模随图大小呈近线性增长，且TANGCO^pre无需针对目标网络单独训练即可对新网络进行分配，部署成本与手动设计的启发式方法相当。不含GNN的自由向量变体表现接近启发式方法，因此图表示是其超越数值搜索的关键。最后，对学习到的分配方案的分析明确了局部风险足够的情形，提出了一种改进的闭式启发式方法，并揭示了仍需拓扑感知学习策略的场景。

英文摘要

Networked systems, from power grids to traffic networks and cloud clusters, carry loads across nodes with limited capacity. A node whose load exceeds its capacity fails and sheds its load onto its neighbors, which can trigger a system-wide cascade. We study how to allocate a fixed capacity budget across nodes to resist these cascades under local load redistribution. The problem is difficult because no optimal allocation is known, and the fail-or-survive objective is non-differentiable and piecewise constant, so exact and gradient-based optimization methods do not directly apply. We introduce TANGCO (Topology-Aware Neural Graph-Guided Capacity Optimization), which uses a graph neural network policy trained through the cascade simulator with policy-gradient learning and a heuristic anchor. We evaluate TANGCO on five synthetic graph families and five real networks spanning power, road, air, and Internet topologies. The learned policy improves on the best of four hand-designed heuristics in all 450 synthetic instances and in 40 of 45 real-network conditions, with robustness gains ranging from 1.6% to 246%. The learned policies transfer to unseen graphs within a family and partially across related topologies, and TANGCO$^{pre}$, pre-trained on synthetic graphs, matches per-network training on unseen real networks. Training scales near-linearly with graph size, and TANGCO$^{pre}$ allocates on a new network with no per-target training, matching the deployment cost of a hand-designed heuristic. Free-vector variants without the GNN, stay close to the heuristics, so the graph representation carries the gain beyond numerical search. Finally, analysis of the learned allocations identifies when local risk is sufficient, leads to an improved closed-form heuristic, and reveals the regimes where a topology-aware learned policy remains necessary.

发表机构

• Carnegie Mellon University（卡内基梅隆大学）

机构由 AI 辅助整理，请以论文原文为准。