Tu, Lingzhuo (2024) Optimizing Routing Strategy in Software Defined Networking. Masters thesis, University of Wales Trinity Saint David.
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Lingzhuo_Tu_MSc_Thesis.pdf - Accepted Version Available under License CC-BY-NC-ND Creative Commons Attribution Non-commercial No Derivatives. Download (5MB) | Preview |
Abstract
Traditional Network Architecture (TNA) is becoming inadequate due to its rigid, hardware-centric configurations, especially in environments where network conditions are highly variable. This has led to increased latency, congestion, and packet loss rates. This project aims to develop an optimized routing strategy for Software Defined Networking (SDN) that leverages machine learning techniques to enhance network traffic management's adaptability and efficiency. The project employs a combination of Dueling Deep Q-Networks (Dueling DQN) and real-time traffic state predictions to create a dynamic routing strategy. The methodology includes extensive simulation using SDN environments to evaluate the performance improvements over traditional routing methods. Preliminary results indicate that the proposed SDN-based routing strategy not only responds more efficiently to dynamic network conditions but also significantly optimizes performance metrics such as bandwidth utilization, latency reduction, and packet loss. The integration of Dueling DQN and real-time traffic predictions within SDN frameworks could potentially redefine network performance standards, offering a more adaptive, efficient, and robust network management system. This study contributes to the field by providing a scalable solution to the complexities of modern network environments, supporting the ongoing evolution of network infrastructure management.
| Item Type: | Thesis (Masters) |
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| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Theses and Dissertations > Masters Dissertations |
| Depositing User: | Victoria Hankinson |
| Date Deposited: | 09 Jan 2025 16:39 |
| Last Modified: | 09 Jan 2025 16:39 |
| URI: | https://repository.uwtsd.ac.uk/id/eprint/3310 |
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