Can AI optimize SDN load balancing?
A Transformer-Based Deep Q-Learning Approach for Dynamic Load Balancing in Software-Defined Networks
January 28, 2025
https://arxiv.org/pdf/2501.12829This paper proposes a new method for dynamic load balancing in software-defined networks (SDNs). It uses a Temporal Fusion Transformer (TFT) to predict future network traffic and a Deep Q-Network (DQN) to make real-time routing decisions based on those predictions. This combined approach aims to improve network performance by maximizing throughput while minimizing latency and packet loss.
Key points for LLM-based multi-agent systems:
- The TFT, proficient at handling long-range dependencies in time series data, can be applied to predict future states and actions in multi-agent scenarios.
- The DQN's ability to learn optimal routing policies translates to training agents to make effective decisions within a complex, dynamic environment, similar to how LLMs can generate contextually appropriate responses.
- The paper demonstrates the power of combining predictive models (like TFT or LLMs) with reinforcement learning (DQN) for optimizing agent behavior within a multi-agent setting. This suggests potential for using LLMs for prediction in multi-agent applications controlled by reinforcement learning algorithms.
- The dynamic load balancing problem in SDNs mirrors challenges in managing resources and communication within a multi-agent system.
- The evaluation metrics used (throughput, latency, packet loss) provide analogues for assessing efficiency and communication effectiveness in multi-agent systems.