摘 要
随着信息技术的迅猛发展，网络规模不断扩大，网络性能监测与优化成为确保网络服务质量的关键。本研究聚焦于网络工程中的网络性能监测与优化方法，旨在解决现有技术在复杂网络环境下监测不精准、优化效果不佳的问题。通过对多种网络协议和流量特征进行深入分析，提出了一种基于深度学习的智能监测框架，该框架能够实时感知网络状态并自动识别异常流量模式。同时，引入了自适应带宽分配算法，根据实时监测结果动态调整网络资源分配，有效提高了网络传输效率。实验结果表明，所提出的监测方法能将误报率降低30%，优化后的网络平均延迟减少了25%，丢包率降低了18%。
关键词：网络性能监测 深度学习 自适应带宽分配
Abstract
With the rapid development of information technology and the continuous expansion of network scale, network performance monitoring and optimization have become the key to ensure the quality of network service. This study focuses on network performance monitoring and optimization methods in network engineering, aiming to solve the problems of inaccurate monitoring and poor optimization effect in complex network environment. Through in-depth analysis of various network protocols and traffic characteristics, we propose a deep learning-based intelligent monitoring fr amework, which can sense network states in real time and automatically identify abnormal traffic patterns. At the same time, the adaptive bandwidth allocation algorithm is introduced to dynamically adjust the network resource allocation according to the real-time monitoring results, effectively improving the network transmission efficiency. The experimental results show that the proposed monitoring method can reduce the false positive rate by 30%, reduce the optimized network average latency by 25%, and reduce the packet loss rate by 18%.
Keyword:Network Performance Monitoring Deep Learning Adaptive Bandwidth Allocation
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本文研究方法 2
2网络性能监测技术分析 2
2.1监测指标体系构建 2
2.2数据采集与预处理 3
2.3性能监测工具与平台 3
3网络性能评估模型研究 4
3.1评估模型理论基础 4
3.2关键性能指标分析 5
3.3模型验证与优化 5
4网络性能优化策略探讨 6
4.1带宽管理与分配 6
4.2流量控制与调度 6
4.3故障预测与恢复 7
结论 8
参考文献 9
致谢 10
随着信息技术的迅猛发展，网络规模不断扩大，网络性能监测与优化成为确保网络服务质量的关键。本研究聚焦于网络工程中的网络性能监测与优化方法，旨在解决现有技术在复杂网络环境下监测不精准、优化效果不佳的问题。通过对多种网络协议和流量特征进行深入分析，提出了一种基于深度学习的智能监测框架，该框架能够实时感知网络状态并自动识别异常流量模式。同时，引入了自适应带宽分配算法，根据实时监测结果动态调整网络资源分配，有效提高了网络传输效率。实验结果表明，所提出的监测方法能将误报率降低30%，优化后的网络平均延迟减少了25%，丢包率降低了18%。
关键词：网络性能监测 深度学习 自适应带宽分配
Abstract
With the rapid development of information technology and the continuous expansion of network scale, network performance monitoring and optimization have become the key to ensure the quality of network service. This study focuses on network performance monitoring and optimization methods in network engineering, aiming to solve the problems of inaccurate monitoring and poor optimization effect in complex network environment. Through in-depth analysis of various network protocols and traffic characteristics, we propose a deep learning-based intelligent monitoring fr amework, which can sense network states in real time and automatically identify abnormal traffic patterns. At the same time, the adaptive bandwidth allocation algorithm is introduced to dynamically adjust the network resource allocation according to the real-time monitoring results, effectively improving the network transmission efficiency. The experimental results show that the proposed monitoring method can reduce the false positive rate by 30%, reduce the optimized network average latency by 25%, and reduce the packet loss rate by 18%.
Keyword:Network Performance Monitoring Deep Learning Adaptive Bandwidth Allocation
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本文研究方法 2
2网络性能监测技术分析 2
2.1监测指标体系构建 2
2.2数据采集与预处理 3
2.3性能监测工具与平台 3
3网络性能评估模型研究 4
3.1评估模型理论基础 4
3.2关键性能指标分析 5
3.3模型验证与优化 5
4网络性能优化策略探讨 6
4.1带宽管理与分配 6
4.2流量控制与调度 6
4.3故障预测与恢复 7
结论 8
参考文献 9
致谢 10
