Topology impacts important network performance metrics, including link utilization, throughput and latency, and is of central importance to network operators. However, due to the combinatorial nature of network topology, it is extremely difficult to obtain an optimal solution, especially since topology planning in networks also often comes with management-specific constraints. As a result, local optimization with hand-tuned heuristic methods from human experts is often adopted in practice. Yet, heuristic methods cannot cover the global topology design space while taking into account constraints, and cannot guarantee to find good solutions. In this paper, we propose a novel deep reinforcement learning (DRL) algorithm for graph searching, called DRL-GS, for network topology optimization. DRL-GS consists of three novel components, including a verifier to validate the correctness of a generated network topology, a graph neural network (GNN) to efficiently approximate topology rating, and a DRL agent to conduct a topology search. DRL-GS can efficiently search over relatively large topology space and output topology with satisfactory performance. We conduct a case study based on a real-world network scenario, and our experimental results demonstrate the superior performance of DRL-GS in terms of both efficiency and performance.

翻译：拓扑结构影响链路利用率、吞吐量和延迟等重要网络性能指标，对网络运营商至关重要。然而，由于网络拓扑的组合特性，获取最优解极为困难，尤其网络拓扑规划通常还伴随着管理相关的特定约束。因此，实践中常采用人工专家手动调整的启发式方法进行局部优化。但启发式方法无法在考虑约束条件的同时覆盖全局拓扑设计空间，也不能保证找到优质解。本文提出一种用于网络拓扑优化的新型深度强化学习图搜索算法DRL-GS。该算法包含三个创新组件：验证生成网络拓扑正确性的验证器、高效近似拓扑评分的图神经网络，以及执行拓扑搜索的深度强化学习智能体。DRL-GS能够高效搜索相对庞大的拓扑空间，并输出具有满意性能的拓扑结构。基于真实网络场景的案例研究表明，DRL-GS在效率与性能方面均展现出优越性。