摘 要：
随着云计算和大数据技术的迅猛发展，大规模数据中心已成为现代互联网服务的核心基础设施，其网络流量管理面临前所未有的挑战。传统网络流量工程方法在应对动态流量模式、复杂拓扑结构以及高带宽需求时表现出明显不足，因此亟需开发更高效、智能化的优化策略。本研究旨在通过结合机器学习与网络优化理论，提出一种面向大规模数据中心的自适应网络流量工程优化框架。该框架利用深度强化学习算法对流量分布进行预测，并通过多目标优化模型实现链路负载均衡与能耗最小化之间的权衡。实验结果表明，所提方法能够显著降低网络拥塞率，提升吞吐量约25%，同时减少能源消耗达18%。此外，本研究还设计了一种分布式控制机制以支持超大规模网络环境下的实时流量调度，有效缓解了集中式控制带来的性能瓶颈问题。创新点在于首次将深度强化学习引入数据中心网络流量优化领域，并提出了兼顾效率与公平性的新型优化目标函数。研究成果为未来数据中心网络的设计与运营提供了重要参考，具有较高的实际应用价值。
关键词：数据中心网络；深度强化学习；网络流量工程；多目标优化；分布式控制机制
Network Traffic Engineering Optimization for Large-Scale Data Centers
Abstract: With the rapid development of cloud computing and big data technologies, large-scale data centers have become the core infrastructure of modern Internet services, facing unprecedented challenges in network traffic management. Traditional network traffic engineering methods show significant limitations when dealing with dynamic traffic patterns, complex topologies, and high bandwidth demands, thus necessitating the development of more efficient and intelligent optimization strategies. This study aims to propose an adaptive network traffic engineering optimization fr amework for large-scale data centers by integrating machine learning with network optimization theory. The fr amework employs deep reinforcement learning algorithms to predict traffic distribution and utilizes a multi-ob jective optimization model to balance link load balancing and energy consumption minimization. Experimental results demonstrate that the proposed method can substantially reduce network congestion rates, increase throughput by approximately 25%, and decrease energy consumption by up to 18%. Furthermore, this research designs a distributed control mechanism to support real-time traffic scheduling in ultra-large network environments, effectively alleviating performance bottlenecks caused by centralized control. The innovation lies in the first application of deep reinforcement learning to the field of data center network traffic optimization and the proposal of a novel ob jective function that considers both efficiency and fairness. The research findings provide critical references for the design and operation of future data center networks, showcasing substantial practical application value.
Keywords: Data Center Network; Deep Reinforcement Learning; Network Traffic Engineering; Multi-ob jective Optimization; Distributed Control Mechanism
目 录
1绪论 1
1.1大规模数据中心发展背景 1
1.2网络流量工程优化的意义 1
1.3国内外研究现状分析 1
1.4本文研究方法与技术路线 2
2数据中心网络架构与流量特性分析 2
2.1数据中心网络架构概述 2
2.2流量模式与分布特征 3
2.3关键性能指标定义 3
2.4流量预测模型构建 4
2.5架构对流量优化的影响 4
3网络流量工程优化算法设计 5
3.1优化问题建模与目标函数 5
3.2基于机器学习的流量调度算法 5
3.3路由策略优化方法研究 6
3.4资源分配与负载均衡分析 6
3.5算法复杂度与可行性评估 7
4实验验证与结果分析 7
4.1实验环境与数据集构建 7
4.2性能测试与对比分析 8
4.3不同场景下的优化效果评估 8
4.4算法鲁棒性与可扩展性研究 9
4.5实验结论与改进建议 9
结论 10
参考文献 11
致 谢 12
