摘 要
随着网络规模的不断扩大和业务类型的日益多样化，传统局域网流量管理面临诸多挑战，如难以灵活应对突发流量、资源分配不合理等，基于软件定义网络（SDN）的局域网流量管理与优化策略成为解决上述问题的关键。本研究旨在构建一种高效、智能且灵活的局域网流量管理体系，以提高网络资源利用率并保障服务质量。通过深入分析SDN架构下局域网流量特性，提出了一种融合深度学习算法的流量预测模型，该模型能够准确预测未来流量趋势，为流量调度提供依据。同时设计了基于多目标优化理论的流量调度算法，综合考虑带宽、时延等多方面因素，在保证关键业务优先级的前提下实现全局最优配置。实验结果表明，所提出的流量预测模型预测精度较高，平均绝对误差低于5%，流量调度算法可使网络整体吞吐量提升约30%，丢包率降低约40%。
关键词：软件定义网络 局域网流量管理 深度学习
Abstract
With the continuous expansion of network scale and the increasing diversification of business types, the traditional LAN traffic management faces many challenges, such as difficult to flexibly respond to sudden traffic, unreasonable resource allocation, etc. The LAN traffic management and optimization strategy based on software-defined network (SDN) has become the key to solve the above problems. The study aims to build an efficient, intelligent and flexible LAN traffic management system to improve the utilization of network resources and guarantee the service quality. Through thorough analysis of LAN traffic characteristics under SDN architecture, a traffic prediction model integrating deep learning algorithm is proposed, which can accurately predict the future traffic trend and provide a basis for traffic scheduling. At the same time, the traffic scheduling algorithm based on multi-ob jective optimization theory is designed, considering various factors such as bandwidth and delay, to realize the global optimal configuration on the premise of ensuring the priority of key services. The experimental results show that the proposed traffic prediction model has high prediction accuracy, the average absolute error is less than 5%, and the traffic scheduling algorithm can improve the overall network throughput by about 30% and reduce the packet loss rate by about 40%.
Keyword:Software Defined Networking Local Area Network Traffic Management Deep Learning
目 录
1绪论 1
1.1研究背景与意义 1
1.2国内外研究现状 1
1.3研究方法与技术路线 2
2SDN架构下的流量管理基础 2
2.2流量管理的基本概念 2
2.3流量分类与优先级设定 3
2.4流量监测与分析方法 3
3基于SDN的局域网流量优化策略 4
3.1流量调度算法设计 4
3.2资源分配与负载均衡 4
3.3带宽优化与服务质量 5
3.4安全性与隐私保护 5
4实验验证与结果分析 6
4.1实验环境搭建 6
4.2测试方案与指标 7
4.3实验结果分析 7
4.4优化效果评估 8
结论 8
参考文献 10
致谢 11
随着网络规模的不断扩大和业务类型的日益多样化，传统局域网流量管理面临诸多挑战，如难以灵活应对突发流量、资源分配不合理等，基于软件定义网络（SDN）的局域网流量管理与优化策略成为解决上述问题的关键。本研究旨在构建一种高效、智能且灵活的局域网流量管理体系，以提高网络资源利用率并保障服务质量。通过深入分析SDN架构下局域网流量特性，提出了一种融合深度学习算法的流量预测模型，该模型能够准确预测未来流量趋势，为流量调度提供依据。同时设计了基于多目标优化理论的流量调度算法，综合考虑带宽、时延等多方面因素，在保证关键业务优先级的前提下实现全局最优配置。实验结果表明，所提出的流量预测模型预测精度较高，平均绝对误差低于5%，流量调度算法可使网络整体吞吐量提升约30%，丢包率降低约40%。
关键词：软件定义网络 局域网流量管理 深度学习
Abstract
With the continuous expansion of network scale and the increasing diversification of business types, the traditional LAN traffic management faces many challenges, such as difficult to flexibly respond to sudden traffic, unreasonable resource allocation, etc. The LAN traffic management and optimization strategy based on software-defined network (SDN) has become the key to solve the above problems. The study aims to build an efficient, intelligent and flexible LAN traffic management system to improve the utilization of network resources and guarantee the service quality. Through thorough analysis of LAN traffic characteristics under SDN architecture, a traffic prediction model integrating deep learning algorithm is proposed, which can accurately predict the future traffic trend and provide a basis for traffic scheduling. At the same time, the traffic scheduling algorithm based on multi-ob jective optimization theory is designed, considering various factors such as bandwidth and delay, to realize the global optimal configuration on the premise of ensuring the priority of key services. The experimental results show that the proposed traffic prediction model has high prediction accuracy, the average absolute error is less than 5%, and the traffic scheduling algorithm can improve the overall network throughput by about 30% and reduce the packet loss rate by about 40%.
Keyword:Software Defined Networking Local Area Network Traffic Management Deep Learning
目 录
1绪论 1
1.1研究背景与意义 1
1.2国内外研究现状 1
1.3研究方法与技术路线 2
2SDN架构下的流量管理基础 2
2.2流量管理的基本概念 2
2.3流量分类与优先级设定 3
2.4流量监测与分析方法 3
3基于SDN的局域网流量优化策略 4
3.1流量调度算法设计 4
3.2资源分配与负载均衡 4
3.3带宽优化与服务质量 5
3.4安全性与隐私保护 5
4实验验证与结果分析 6
4.1实验环境搭建 6
4.2测试方案与指标 7
4.3实验结果分析 7
4.4优化效果评估 8
结论 8
参考文献 10
致谢 11
