Recently, distributed controller architectures have been quickly gaining popularity in Software-Defined Networking (SDN). However, the use of distributed controllers introduces a new and important Request Dispatching (RD) problem with the goal for every SDN switch to properly dispatch their requests among all controllers so as to optimize network performance. This goal can be fulfilled by designing an RD policy to guide distribution of requests at each switch. In this paper, we propose a Multi-Agent Deep Reinforcement Learning (MA-DRL) approach to automatically design RD policies with high adaptability and performance. This is achieved through a new problem formulation in the form of a Multi-Agent Markov Decision Process (MA-MDP), a new adaptive RD policy design and a new MA-DRL algorithm called MA-PPO. Extensive simulation studies show that our MA-DRL technique can effectively train RD policies to significantly outperform man-made policies, model-based policies, as well as RD policies learned via single-agent DRL algorithms.

中文翻译：

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分布式控制器软件定义网络中请求分配的多智能体深度强化学习

最近，分布式控制器体系结构已在软件定义网络（SDN）中迅速获得普及。但是，分布式控制器的使用引入了一个新的重要的请求分配（RD）问题，目的是使每个SDN交换机都能在所有控制器之间正确分配其请求，从而优化网络性能。通过设计RD策略来指导每个交换机的请求分配，可以实现此目标。在本文中，我们提出了一种多代理深度强化学习（MA-DRL）方法，以自动设计具有高适应性和高性能的RD策略。这是通过以多智能体马尔可夫决策过程（MA-MDP）形式，新的自适应RD策略设计和新的称为MA-PPO的MA-DRL算法的形式提出的新问题来实现的。