A transformer-based deep q learning approach for dynamic load balancing in software-defined networks

2025-01-22

AI Summary

To address load imbalance caused by dynamic traffic in software-defined networking (SDN), this paper proposes a prediction-decision co-optimization framework integrating Temporal Fusion Transformer (TFT) and Deep Q-Network (DQN). It is the first work to employ TFT for fine-grained SDN traffic forecasting and to leverage its predictions for adaptive routing scheduling via DQN, overcoming the inflexibility of conventional static policies such as Round Robin (RR) and Weighted Round Robin (WRR). Evaluated in Mininet under 500 MB/s and 1000 MB/s data rates, the framework achieves throughputs of 0.275 and 0.281, respectively—substantially outperforming RR/WRR—while simultaneously reducing average latency and packet loss rate, demonstrating effectiveness and robustness under highly dynamic conditions. The core contribution lies in establishing an end-to-end learnable intelligent load balancing paradigm that jointly enables accurate traffic prediction and real-time decision-making.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Learning to Search
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

This study proposes a novel approach for dynamic load balancing in Software-Defined Networks (SDNs) using a Transformer-based Deep Q-Network (DQN). Traditional load balancing mechanisms, such as Round Robin (RR) and Weighted Round Robin (WRR), are static and often struggle to adapt to fluctuating traffic conditions, leading to inefficiencies in network performance. In contrast, SDNs offer centralized control and flexibility, providing an ideal platform for implementing machine learning-driven optimization strategies. The core of this research combines a Temporal Fusion Transformer (TFT) for accurate traffic prediction with a DQN model to perform real-time dynamic load balancing. The TFT model predicts future traffic loads, which the DQN uses as input, allowing it to make intelligent routing decisions that optimize throughput, minimize latency, and reduce packet loss. The proposed model was tested against RR and WRR in simulated environments with varying data rates, and the results demonstrate significant improvements in network performance. For the 500MB data rate, the DQN model achieved an average throughput of 0.275 compared to 0.202 and 0.205 for RR and WRR, respectively. Additionally, the DQN recorded lower average latency and packet loss. In the 1000MB simulation, the DQN model outperformed the traditional methods in throughput, latency, and packet loss, reinforcing its effectiveness in managing network loads dynamically. This research presents an important step towards enhancing network performance through the integration of machine learning models within SDNs, potentially paving the way for more adaptive, intelligent network management systems.

Problem

• Software Defined Networking
• Traffic Allocation
• Resource Scheduling

Innovation

• Software-Defined Networking (SDN)
• Transformer Model
• Deep Q-Network (DQN)