摘 要
随着互联网规模的不断扩大和网络应用类型的日益丰富，计算机网络中的数据流量呈现出爆发式增长，网络拥塞问题愈发严重，这不仅影响了网络性能，还制约了服务质量的提升。为此，对计算机网络中的拥塞控制算法展开分析与改进研究。本研究旨在深入剖析现有拥塞控制算法，如TCP拥塞控制算法等，在不同网络环境下的表现情况，包括算法在应对突发流量、多路径传输以及异构网络融合场景中存在的不足之处。基于此，提出一种融合智能预测机制与自适应调整策略的新型拥塞控制算法。该算法利用机器学习模型对未来网络状态进行预测，并根据预测结果动态调整拥塞窗口大小、传输速率等关键参数，以实现更精准地控制网络流量。
关键词：网络拥塞控制 TCP拥塞控制算法 智能预测机制
Abstract
With the continuous expansion of the Internet scale and the increasingly enrichment of network application types, the data traffic in the computer network shows explosive growth, and the network congestion problem becomes more and more serious, which not only affects the network performance, but also restricts the improvement of service quality. Therefore, we analyze and improve the congestion control algorithm in computer network. This study aims to deeply analyze the performance of existing congestion control algorithms, such as TCP congestion control algorithms, in different network environments, including the shortcomings of the algorithm in dealing with sudden traffic, multi-path transmission and heterogeneous network fusion scenarios. Based on this, a new congestion control algorithm integrating intelligent prediction mechanism and adaptive adjustment strategy is proposed. The algorithm uses the machine learning model to predict the future network state, and dynamically adjusts the key parameters such as the congestion window size and transmission rate according to the prediction results, so as to achieve more accurate control of network traffic.
Keyword:Network Congestion Control Tcp Congestion Control Algorithm Intelligent Prediction Mechanism
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
2拥塞控制算法原理分析 2
2.1拥塞产生的原因及影响 2
2.2典型拥塞控制算法解析 3
2.3拥塞控制算法性能评估指标 3
3现有拥塞控制算法的局限性 4
3.1传统算法在网络环境变化中的不足 4
3.2复杂网络环境下算法适应性问题 4
3.3算法在不同应用场景下的表现差异 5
4拥塞控制算法改进方案 6
4.1改进思路与设计原则 6
4.2新型拥塞控制算法模型构建 6
4.3改进算法的仿真与性能测试 7
结论 8
参考文献 9
致谢 10
随着互联网规模的不断扩大和网络应用类型的日益丰富，计算机网络中的数据流量呈现出爆发式增长，网络拥塞问题愈发严重，这不仅影响了网络性能，还制约了服务质量的提升。为此，对计算机网络中的拥塞控制算法展开分析与改进研究。本研究旨在深入剖析现有拥塞控制算法，如TCP拥塞控制算法等，在不同网络环境下的表现情况，包括算法在应对突发流量、多路径传输以及异构网络融合场景中存在的不足之处。基于此，提出一种融合智能预测机制与自适应调整策略的新型拥塞控制算法。该算法利用机器学习模型对未来网络状态进行预测，并根据预测结果动态调整拥塞窗口大小、传输速率等关键参数，以实现更精准地控制网络流量。
关键词：网络拥塞控制 TCP拥塞控制算法 智能预测机制
Abstract
With the continuous expansion of the Internet scale and the increasingly enrichment of network application types, the data traffic in the computer network shows explosive growth, and the network congestion problem becomes more and more serious, which not only affects the network performance, but also restricts the improvement of service quality. Therefore, we analyze and improve the congestion control algorithm in computer network. This study aims to deeply analyze the performance of existing congestion control algorithms, such as TCP congestion control algorithms, in different network environments, including the shortcomings of the algorithm in dealing with sudden traffic, multi-path transmission and heterogeneous network fusion scenarios. Based on this, a new congestion control algorithm integrating intelligent prediction mechanism and adaptive adjustment strategy is proposed. The algorithm uses the machine learning model to predict the future network state, and dynamically adjusts the key parameters such as the congestion window size and transmission rate according to the prediction results, so as to achieve more accurate control of network traffic.
Keyword:Network Congestion Control Tcp Congestion Control Algorithm Intelligent Prediction Mechanism
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
2拥塞控制算法原理分析 2
2.1拥塞产生的原因及影响 2
2.2典型拥塞控制算法解析 3
2.3拥塞控制算法性能评估指标 3
3现有拥塞控制算法的局限性 4
3.1传统算法在网络环境变化中的不足 4
3.2复杂网络环境下算法适应性问题 4
3.3算法在不同应用场景下的表现差异 5
4拥塞控制算法改进方案 6
4.1改进思路与设计原则 6
4.2新型拥塞控制算法模型构建 6
4.3改进算法的仿真与性能测试 7
结论 8
参考文献 9
致谢 10
