We propose a machine learning approach to the optimal control of multiclass
fluid queueing networks (MFQNETs) that provides explicit and insightful control
policies. We prove that a threshold type optimal policy exists for MFQNET
control problems, where the threshold curves are hyperplanes passing through
the origin. We use Optimal Classification Trees with hyperplane splits (OCT-H)
to learn an optimal control policy for MFQNETs. We use numerical solutions of
MFQNET control problems as a training set and apply OCT-H to learn explicit
control policies. We report experimental results with up to 33 servers and 99
classes that demonstrate that the learned policies achieve 100\% accuracy on
the test set. While the offline training of OCT-H can take days in large
networks, the online application takes milliseconds.

我们提出了一种机器学习方法来解决多类流体排队网络（MFQNETs）的最优控制问题，并提供了明确和深入的控制策略。我们证明了 MFQNET 控制问题存在一种阈值类型的最优策略，其中阈值曲线是通过原点的超平面。我们使用具有超平面分割的最优分类树（OCT-H）来学习 MFQNETs 的最优控制策略。我们使用 MFQNET 控制问题的数值解作为训练集，并将 OCT-H 应用于学习明确的控制策略。实验结果报告了高达 33 个服务器和 99 个类别的实验结果，表明学习到的策略在测试集上达到 100％的准确率。虽然在大型网络中，OCT-H 的离线训练可能需要几天时间，但在线应用只需要毫秒级的时间。