摘要
随着网络规模的不断扩大和业务类型的日益多样化，传统网络架构在流量管理方面逐渐暴露出灵活性不足、配置复杂等弊端，软件定义网络（SDN）架构应运而能有效解决这些问题。本研究旨在探索SDN架构下的流量管理策略，以提高网络资源利用率、优化服务质量并增强网络可扩展性为目的。基于SDN控制与转发分离的特性，采用理论分析与仿真实验相结合的方法，构建了包含流量识别、分类、调度等模块的流量管理系统模型，提出了一种自适应流量调度算法，可根据实时网络状态动态调整流量路径。结果表明，该算法能够显著降低网络拥塞概率，减少平均时延，提高吞吐量。本研究创新性地将机器学习算法引入流量预测环节，实现了对流量趋势的精准预判，从而为提前规划流量路径提供依据。这一成果不仅丰富了SDN流量管理领域的理论体系，而且为实际网络部署提供了有效的技术手段，有助于推动SDN技术在网络运营中的广泛应用。
关键词：软件定义网络；流量管理；自适应调度算法；机器学习；网络拥塞控制
Abstract
As the scale of networks continues to expand and the diversity of service types increases, traditional network architectures have increasingly revealed limitations in traffic management, such as insufficient flexibility and complex configuration. Software-Defined Networking (SDN) architecture has emerged as an effective solution to these issues. This study aims to explore traffic management strategies under the SDN fr amework with the ob jectives of improving network resource utilization, optimizing service quality, and enhancing network scalability. Leveraging the characteristic separation of control and forwarding in SDN, this research employs a combination of theoretical analysis and simulation experiments to construct a traffic management system model that includes modules for traffic identification, classification, and scheduling. An adaptive traffic scheduling algorithm is proposed, which can dynamically adjust traffic paths based on real-time network conditions. The results indicate that this algorithm significantly reduces the probability of network congestion, decreases average latency, and increases throughput. Innovatively, machine learning algorithms are introduced into the traffic prediction phase, achieving precise forecasting of traffic trends and providing a basis for preemptive planning of traffic paths. This achievement not only enriches the theoretical fr amework of SDN traffic management but also offers effective technical means for practical network deployment, thereby promoting the widespread application of SDN technology in network operations.
Keywords：Software Defined Networking; Traffic Management; Adaptive Scheduling Algorithm; Machine Learning; Network Congestion Control
目 录
摘要 I
Abstract II
一、绪论 1
(一) 研究背景与意义 1
(二) 国内外研究现状 1
二、软件定义网络架构分析 2
(一) SDN架构基本原理 2
(二) SDN架构中的控制器 2
三、流量管理策略设计 2
(一) 流量分类与优先级设定 2
(二) 动态带宽分配机制 3
(三) 流量调度算法优化 4
四、策略实施与效果评估 5
(一) 实验环境搭建 5
(二) 策略实施过程 6
(三) 性能测试与结果分析 6
结 论 8
参考文献 9
