摘 要：
随着网络规模的不断扩大和业务需求的日益多样化，传统静态带宽分配机制已难以满足动态变化的流量需求，基于软件定义网络（SDN）的动态带宽分配机制成为研究热点本研究旨在通过结合SDN架构的集中控制特性和先进的流量预测算法，设计一种高效、灵活且适应性强的网络带宽动态分配机制研究提出了一种基于机器学习的流量预测模型，能够实时分析网络流量特征并预测未来流量需求，同时设计了基于优先级的带宽分配策略，确保关键业务的服务质量通过构建仿真环境对所提机制进行验证，结果表明该机制能够在多种复杂网络场景下显著提升带宽利用率，降低延迟和丢包率与传统分配机制相比，新机制的带宽利用率平均提升了25%，关键业务的延迟降低了30%以上此外，研究还针对大规模网络环境下的扩展性问题进行了优化，进一步增强了机制的实际应用价值本研究的主要贡献在于提出了融合流量预测与优先级分配的创新方法，为解决动态网络环境中的带宽管理难题提供了有效途径，同时为SDN在实际网络部署中的性能优化提供了理论支持和实践参考
关键词：软件定义网络；动态带宽分配；流量预测；机器学习；优先级策略
A Dynamic Bandwidth Allocation Mechanism Based on Software-Defined Networking
Abstract: With the continuous expansion of network scale and the increasing diversification of service requirements, traditional static bandwidth allocation mechanisms are no longer capable of meeting dynamically changing traffic demands. As a result, dynamic bandwidth allocation mechanisms based on Software-Defined Networking (SDN) have become a research hotspot. This study aims to design an efficient, flexible, and adaptable network bandwidth allocation mechanism by leveraging the centralized control characteristics of the SDN architecture and advanced traffic prediction algorithms. A machine-learning-based traffic prediction model is proposed, which can analyze network traffic features in real time and forecast future traffic demands. Simultaneously, a priority-based bandwidth allocation strategy is designed to ensure the quality of service for critical services. The proposed mechanism is validated through a simulation environment, and the results demonstrate its ability to significantly enhance bandwidth utilization and reduce latency and packet loss rates across various complex network scenarios. Compared with traditional allocation mechanisms, the new mechanism achieves an average improvement of 25% in bandwidth utilization and reduces latency for critical services by over 30%. Additionally, this study addresses scalability issues in large-scale network environments, further enhancing the practical application value of the mechanism. The primary contribution of this research lies in proposing an innovative method that integrates traffic prediction and priority-based allocation, providing an effective solution to bandwidth management challenges in dynamic network environments. Furthermore, it offers theoretical support and practical references for optimizing the performance of SDN in real-world network deployments.
Keywords: Software-Defined Networking; Dynamic Bandwidth Allocation; Traffic Prediction; Machine Learning; Priority Strategy
目 录
1绪论 1
1.1软件定义网络背景与意义 1
1.2动态带宽分配的研究现状 1
1.3本文研究方法与技术路线 2
2软件定义网络基础与动态分配需求分析 2
2.1SDN架构及其关键技术 2
2.2网络带宽动态分配的需求场景 3
2.3带宽分配中的挑战与问题分析 3
2.4动态分配机制的设计目标与约束条件 4
2.5SDN在带宽管理中的优势与局限 4
3动态带宽分配算法设计与优化 5
3.1基于流量预测的分配模型构建 5
3.2实时流量监测与数据采集 5
3.3动态带宽分配算法 6
3.4动态带宽分配机制 6
4系统实现与实验验证 7
4.1动态分配系统的架构设计 7
4.2控制平面与数据平面的协同机制 7
4.3实验环境搭建与测试方案设计 8
4.4实验结果分析与性能评估 8
4.5系统改进方向与未来展望 8
结论 9
参考文献 11
致 谢 12
