  In software-defined networking (SDN), the implementation of distributed SDN
controllers, with each controller responsible for managing a specific
sub-network or domain, plays a critical role in achieving a balance between
centralized control, scalability, reliability, and network efficiency. These
controllers must be synchronized to maintain a logically centralized view of
the entire network. While there are various approaches for synchronizing
distributed SDN controllers, most tend to prioritize goals such as optimization
of communication latency or load balancing, often neglecting to address both
the aspects simultaneously. This limitation becomes particularly significant
when considering applications like Augmented and Virtual Reality (AR/VR), which
demand constrained network latencies and substantial computational resources.
Additionally, many existing studies in this field predominantly rely on
value-based reinforcement learning (RL) methods, overlooking the potential
advantages offered by state-of-the-art policy-based RL algorithms. To bridge
this gap, our work focuses on examining deep reinforcement learning (DRL)
techniques, encompassing both value-based and policy-based methods, to
guarantee an upper latency threshold for AR/VR task offloading within SDN
environments, while selecting the most cost-effective servers for AR/VR task
offloading. Our evaluation results indicate that while value-based methods
excel in optimizing individual network metrics such as latency or load
balancing, policy-based approaches exhibit greater robustness in adapting to
sudden network changes or reconfiguration.
