Constrained Reinforcement Learning For Adaptive Controller Synchronization In Distributed SDN

Abstract

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 o

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keypanitsas2024constrained

Related papers

• MACS: Deep Reinforcement Learning Based SDN Controller Synchronization Policy Design (2019)9.92
• Learning To Simulate Self-driven Particles System With Coordinated Policy Optimization (2021)0.00
• Multi-fidelity Reinforcement Learning Control For Complex Dynamical Systems (2025)0.00
• Reinforcement Learning For Control Systems With Time Delays: A Comprehensive Survey (2026)0.00
• Control-optimized Deep Reinforcement Learning For Artificially Intelligent Autonomous Systems (2025)0.00
• Application Of Deep Reinforcement Learning To Event-triggered Control For Networked Artificial Pancreas Systems (2026)0.00
• Reinforcement Learning For Datacenter Congestion Control (2021)0.00
• Distrl: An Asynchronous Distributed Reinforcement Learning Framework For On-device Control Agents (2024)0.00