  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.
