基于深度强化学习的网络规划

Hang Zhu, Varun Gupta, S. Ahuja, Yuandong Tian, Ying Zhang, Xin Jin
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引用次数: 61

摘要

网络规划对web服务的性能、可靠性和成本至关重要。这个问题通常被表述为整数线性规划(ILP)问题。今天的实践依赖于人类专家手动调整的启发式来解决ILP求解器的可扩展性挑战。在本文中,我们提出了神经计划,一种深度强化学习(RL)方法来解决网络规划问题。该问题涉及多步骤决策和成本最小化,可以很自然地归结为深度强化学习问题。我们开发了两种重要的领域特定技术。首先,我们使用图神经网络(GNN)和一种新的特定于领域的节点-链路转换来进行状态编码,以便在规划决策过程中处理不断变化的网络拓扑的动态特性。其次,我们利用两阶段混合方法,首先使用深度强化学习来修剪搜索空间,然后使用ILP求解器来找到最优解。这种方法类似于今天的实践,但在第一阶段避免了人类专家与RL代理。对实际拓扑和大型生产网络设置的评估表明,NeuroPlan可扩展到超出ILP求解器能力的大型拓扑,并且与手动调整的启发式相比,可将成本降低17%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Network planning with deep reinforcement learning
Network planning is critical to the performance, reliability and cost of web services. This problem is typically formulated as an Integer Linear Programming (ILP) problem. Today's practice relies on hand-tuned heuristics from human experts to address the scalability challenge of ILP solvers. In this paper, we propose NeuroPlan, a deep reinforcement learning (RL) approach to solve the network planning problem. This problem involves multi-step decision making and cost minimization, which can be naturally cast as a deep RL problem. We develop two important domain-specific techniques. First, we use a graph neural network (GNN) and a novel domain-specific node-link transformation for state encoding, in order to handle the dynamic nature of the evolving network topology during planning decision making. Second, we leverage a two-stage hybrid approach that first uses deep RL to prune the search space and then uses an ILP solver to find the optimal solution. This approach resembles today's practice, but avoids human experts with an RL agent in the first stage. Evaluation on real topologies and setups from large production networks demonstrates that NeuroPlan scales to large topologies beyond the capability of ILP solvers, and reduces the cost by up to 17% compared to hand-tuned heuristics.
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