摘 要
计算机网络中的拥塞控制是确保网络高效稳定运行的关键技术，随着互联网规模的不断扩大和应用类型的日益多样化，传统拥塞控制算法面临诸多挑战。本研究聚焦于计算机网络中拥塞控制算法，旨在探索适应现代网络环境的新算法。研究基于对现有典型拥塞控制算法如TCP Reno、CUBIC等深入剖析，发现其在网络流量突发性强、异构性明显的情况下存在响应滞后、带宽利用率低等问题。通过引入机器学习方法构建预测模型，结合网络状态实时监测数据，提出一种自适应拥塞控制算法。该算法能够根据网络状况动态调整拥塞窗口大小，有效提高网络资源利用率并降低丢包率。仿真实验结果表明，在不同网络环境下与传统算法相比，新算法可使平均时延减少约30%，吞吐量提升约25%。这不仅解决了当前拥塞控制面临的难题，还为未来复杂网络环境下拥塞控制提供了新的思路，对于优化网络性能、改善用户体验具有重要意义。
关键词：拥塞控制算法 机器学习 自适应调整
Abstract
Congestion control in the computer network is the key technology to ensure the efficient and stable operation of the network. With the continuous expansion of the Internet scale and the increasing diversification of application types, the traditional congestion control algorithm faces many challenges. This study focuses on congestion control algorithms in computer networks and aims to explore new algorithms adapted to modern network environments. Based on the in-depth analysis of the existing typical congestion control algorithms such as TCP Reno and CUBIC, it is found that they have response lag and low bandwidth utilization under the condition of strong network traffic burst and obvious heterogeneity. By introducing machine learning method to construct a prediction model and combining with real-time monitoring data, an adaptive congestion control algorithm is proposed. The algorithm can dynamically adjust the congestion window size according to the network condition, effectively improve the network resource utilization rate and reduce the packet loss rate. The results show that the new algorithm can reduce the average delay by about 30% and improve the throughput by about 25% in different network environments. This not only solves the current problems of congestion control, but also provides new ideas for congestion control in complex network environment in the future, which is of great significance for optimizing network performance and improving user experience.
Keyword:Plug control algorithm Machine learning Machine learning Adaptive adjustment
目 录
1 引言 1
2 拥塞控制算法原理分析 1
2.1 拥塞产生的原因及影响 1
2.2 常见拥塞控制算法分类 2
2.3 拥塞控制算法性能评价指标 2
2.4 拥塞控制算法的数学模型 3
3 典型拥塞控制算法研究 3
3.1 TCP拥塞控制机制剖析 3
3.2 主动队列管理算法研究 4
3.3 显式拥塞通知机制分析 4
3.4 新兴拥塞控制算法探索 5
4 拥塞控制算法优化与应用 6
4.1 拥塞控制算法优化策略 6
4.2 数据中心网络中的应用 6
4.3 物联网环境下的适应性 7
4.4 未来发展方向展望 7
5 结论 8
参考文献 9
致谢 10
计算机网络中的拥塞控制是确保网络高效稳定运行的关键技术，随着互联网规模的不断扩大和应用类型的日益多样化，传统拥塞控制算法面临诸多挑战。本研究聚焦于计算机网络中拥塞控制算法，旨在探索适应现代网络环境的新算法。研究基于对现有典型拥塞控制算法如TCP Reno、CUBIC等深入剖析，发现其在网络流量突发性强、异构性明显的情况下存在响应滞后、带宽利用率低等问题。通过引入机器学习方法构建预测模型，结合网络状态实时监测数据，提出一种自适应拥塞控制算法。该算法能够根据网络状况动态调整拥塞窗口大小，有效提高网络资源利用率并降低丢包率。仿真实验结果表明，在不同网络环境下与传统算法相比，新算法可使平均时延减少约30%，吞吐量提升约25%。这不仅解决了当前拥塞控制面临的难题，还为未来复杂网络环境下拥塞控制提供了新的思路，对于优化网络性能、改善用户体验具有重要意义。
关键词：拥塞控制算法 机器学习 自适应调整
Abstract
Congestion control in the computer network is the key technology to ensure the efficient and stable operation of the network. With the continuous expansion of the Internet scale and the increasing diversification of application types, the traditional congestion control algorithm faces many challenges. This study focuses on congestion control algorithms in computer networks and aims to explore new algorithms adapted to modern network environments. Based on the in-depth analysis of the existing typical congestion control algorithms such as TCP Reno and CUBIC, it is found that they have response lag and low bandwidth utilization under the condition of strong network traffic burst and obvious heterogeneity. By introducing machine learning method to construct a prediction model and combining with real-time monitoring data, an adaptive congestion control algorithm is proposed. The algorithm can dynamically adjust the congestion window size according to the network condition, effectively improve the network resource utilization rate and reduce the packet loss rate. The results show that the new algorithm can reduce the average delay by about 30% and improve the throughput by about 25% in different network environments. This not only solves the current problems of congestion control, but also provides new ideas for congestion control in complex network environment in the future, which is of great significance for optimizing network performance and improving user experience.
Keyword:Plug control algorithm Machine learning Machine learning Adaptive adjustment
目 录
1 引言 1
2 拥塞控制算法原理分析 1
2.1 拥塞产生的原因及影响 1
2.2 常见拥塞控制算法分类 2
2.3 拥塞控制算法性能评价指标 2
2.4 拥塞控制算法的数学模型 3
3 典型拥塞控制算法研究 3
3.1 TCP拥塞控制机制剖析 3
3.2 主动队列管理算法研究 4
3.3 显式拥塞通知机制分析 4
3.4 新兴拥塞控制算法探索 5
4 拥塞控制算法优化与应用 6
4.1 拥塞控制算法优化策略 6
4.2 数据中心网络中的应用 6
4.3 物联网环境下的适应性 7
4.4 未来发展方向展望 7
5 结论 8
参考文献 9
致谢 10
