摘 要
随着信息技术的迅猛发展，计算机网络已成为现代社会不可或缺的基础设施，网络流量呈爆炸式增长，对网络性能提出严峻挑战。为此，本研究聚焦计算机网络中的流量分析与优化，旨在通过深入剖析流量特征，探索有效的优化策略以提升网络性能。基于此目的，采用数据挖掘技术从海量流量数据中提取有价值信息，构建流量预测模型，并结合仿真模拟技术评估不同优化方案的效果。研究发现，通过智能算法识别流量模式并进行动态调度，可显著降低网络拥塞概率，提高带宽利用率。创新性地引入机器学习算法优化传统流量管理机制，在保证服务质量的同时实现资源高效配置。实验结果表明，所提方法较现有方案在网络延迟、丢包率等关键指标上均有明显改善，为解决复杂网络环境下的流量问题提供了新思路。该研究不仅丰富了计算机网络流量管理理论体系，还为实际网络运营提供科学依据和技术支持，具有重要的学术价值和应用前景。
关键词：计算机网络流量优化 数据挖掘与流量预测 机器学习算法
Abstract
With the rapid development of information technology, computer networks have become an indispensable infrastructure in modern society, leading to explosive growth in network traffic and posing significant challenges to network performance. This study focuses on traffic analysis and optimization in computer networks, aiming to explore effective optimization strategies by thoroughly analyzing traffic characteristics to enhance network performance. To achieve this ob jective, data mining techniques are employed to extract valuable information from massive traffic data, constructing traffic prediction models, and simulation technologies are combined to evaluate the effectiveness of different optimization schemes. The study finds that intelligent algorithms can significantly reduce network congestion probability and improve bandwidth utilization by identifying traffic patterns and implementing dynamic scheduling. Innovatively, machine learning algorithms are introduced to optimize traditional traffic management mechanisms, achieving efficient resource allocation while ensuring service quality. Experimental results demonstrate that the proposed methods show noticeable improvements over existing solutions in key metrics such as network latency and packet loss rate, providing new insights into solving traffic issues in complex network environments. This research not only enriches the theoretical system of computer network traffic management but also offers scientific evidence and technical support for practical network operations, holding important academic value and application prospects.
Keyword:Computer Network Traffic Optimization Data Mining And Traffic Prediction Machine Learning Algorithms
目 录
1绪论 1
1.1计算机网络流量分析优化的背景 1
1.2研究意义与价值探讨 1
1.3国内外研究现状综述 1
1.4本文研究方法概述 2
2流量分析技术基础 2
2.1流量监测与数据采集 2
2.2流量特征提取方法 3
2.3流量模式识别技术 3
2.4流量可视化呈现手段 4
3流量优化策略研究 4
3.1带宽资源分配机制 2
3.2拥塞控制算法设计 2
3.3路由选择优化方案 3
3.4流量调度策略制定 3
4实验验证与案例分析 4
4.1实验环境搭建过程 4
4.2测试数据集选取原则 4
4.3实验结果对比分析 5
4.4案例应用效果评估 6
结论 6
参考文献 8
致谢 9
