摘 要
随着网络规模的不断扩大和业务类型的日益复杂，传统网络架构在流量管理方面逐渐暴露出灵活性差、可扩展性弱等问题，软件定义网络（SDN）应运而生。SDN将控制平面与数据平面分离，为流量管理提供了新的思路。本文旨在深入研究SDN中的流量管理策略，以提高网络资源利用率和服务质量。通过分析现有流量管理机制的不足，提出一种基于机器学习的自适应流量调度算法，该算法能够根据实时网络状态动态调整流量路径，有效应对突发流量并优化带宽分配。利用仿真平台对所提算法进行验证，结果表明，在多种网络负载条件下，新算法相较于传统方法在网络延迟、丢包率等关键性能指标上均有显著改善，特别是在高负载场景下优势更为明显。此外，还构建了原型系统并在实际环境中测试，进一步证明了方案的有效性和可行性。本研究创新性地将机器学习技术引入SDN流量管理领域，不仅提升了网络智能管理水平，也为未来相关研究提供了有益参考。
关键词：软件定义网络；流量管理；机器学习；自适应流量调度；网络性能优化
Abstract
As the scale of networks continues to expand and the types of services become increasingly complex, traditional network architectures have gradually exposed issues such as poor flexibility and weak scalability in traffic management. In response to these challenges, Software-Defined Networking (SDN) has emerged, separating the control plane from the data plane and offering a novel approach to traffic management. This study aims to thoroughly investigate traffic management strategies within SDN to enhance network resource utilization and service quality. By analyzing the shortcomings of existing traffic management mechanisms, this paper proposes an adaptive traffic scheduling algorithm based on machine learning. This algorithm dynamically adjusts traffic routes according to real-time network conditions, effectively addressing bursty traffic and optimizing bandwidth allocation. The proposed algorithm was validated using a simulation platform, and the results demonstrate significant improvements over traditional methods in key performance metrics such as network latency and packet loss rate under various network load conditions, with particularly notable advantages in high-load scenarios. Furthermore, a prototype system was constructed and tested in real-world environments, further confirming the effectiveness and feasibility of the solution. This research innovatively integrates machine learning technology into SDN traffic management, not only improving intelligent network management but also providing valuable references for future related studies.
Keywords：Software Defined Networking；Traffic Management；Machine Learning；Adaptive Traffic Scheduling；Network Performance Optimization
目 录
摘 要 I
Abstract II
引 言 1
第一章 SDN流量管理基础理论 2
1.1 流量管理关键概念 2
1.2 传统网络的局限性 2
第二章 SDN中流量分类与优先级 4
2.1 流量分类方法 4
2.2 优先级设定原则 4
2.3 应用场景分析 5
第三章 SDN流量调度算法研究 7
3.1 常见调度算法 7
3.2 算法性能评估 7
3.3 调度优化策略 8
第四章 SDN流量管理安全机制 10
4.1 安全威胁分析 10
4.2 访问控制策略 10
4.3 异常流量检测 11
结 论 13
参考文献 14
致 谢 15
