摘 要
本研究针对云计算环境中流量识别技术的应用与优化进行了深入探讨。随着云计算的快速发展，网络流量的识别和管理变得愈发重要。流量识别技术能够有效区分不同类型的网络流量，为云计算资源分配、安全监控等提供关键信息。本研究旨在提高云计算环境中流量识别的准确性和效率，以满足日益复杂的网络需求。为实现这一目标，我们采用了深度学习算法，结合传统的流量识别方法，对云计算环境中的网络流量进行了细致分析。通过构建卷积神经网络模型，我们处理了海量的网络流量数据，提取了关键特征，并进行了分类训练。此外，我们还对模型进行了优化，以提高其处理速度和准确性。实验结果表明，优化后的深度学习模型在流量识别上表现出了显著的优势。与传统方法相比，该模型不仅提高了识别的准确率，还大幅减少了误报率。特别是在处理加密流量和P2P流量时，模型的性能尤为出色。这一创新性的应用不仅增强了云计算环境的安全性，也为网络资源管理带来了便利。
关键词：云计算环境，流量识别，深度学习算法
ABSTRACT
In this paper, the application and optimization of traffic identification technology in cloud computing environment are discussed. With the rapid development of cloud computing, the identification and management of network traffic has become increasingly important. Traffic identification technology can effectively distinguish different types of network traffic, and provide key information for cloud computing resource allocation and security monitoring. This study aims to improve the accuracy and efficiency of traffic identification in cloud computing environments to meet the increasingly complex network requirements. To achieve this, we employ deep learning algorithms, combined with traditional traffic identification methods, to analyze network traffic in a cloud computing environment in detail. By constructing a convolutional neural network model, we process massive network traffic data, extract key features, and conduct classification training. In addition, we have optimized the model to improve its processing speed and accuracy. The experimental results show that the optimized deep learning model has a significant advantage in traffic recognition. Compared with the traditional method, this model not only improves the recognition accuracy, but also greatly reduces the false positive rate. The performance of the model is particularly good when dealing with encrypted traffic and P2P traffic. This innovative application not only enhances the security of the cloud computing environment, but also brings convenience to network resource management.
Keywords: Cloud computing environment, traffic identification, deep learning algorithm
目 录
摘 要 I
ABSTRACT II
第一章 绪论 1
1.1 研究背景及意义 1
1.2 流量识别技术在云计算中的研究现状 1
第二章 流量识别技术基础 3
2.1 流量识别技术概述 3
2.2 流量识别技术的分类 3
2.3 流量识别技术的原理 4
2.4 流量识别技术的性能指标 4
第三章 云计算环境中的流量识别技术应用 6
3.1 云计算环境特点分析 6
3.2 流量识别技术在云计算中的应用场景 6
3.3 云计算环境中流量识别技术的挑战 7
3.4 典型案例分析 7
第四章 流量识别技术在云计算环境中的优化策略 9
4.1 流量识别技术的优化需求分析 9
4.2 基于云计算的流量识别技术优化方法 9
4.3 优化策略的实验验证与性能评估 10
4.4 优化效果的对比分析 10
结束语 12
谢 辞 13
参考文献 14
