摘 要
随着云计算技术的迅猛发展，云平台面临着日益复杂的资源调度与负载均衡挑战。为提高资源利用率和服务质量，本研究聚焦于云计算环境下的资源调度与负载均衡策略，旨在提出一种高效、智能且适应性强的解决方案。通过分析现有算法在动态性、可扩展性和能耗控制方面的不足，引入深度强化学习框架构建新型调度模型，该模型能够根据实时工作负载特征自动调整参数，实现对计算、存储和网络资源的精细化管理。实验结果表明，所提方法不仅有效降低了平均响应时间约30%，提高了系统吞吐量25%以上，还实现了能源消耗减少15%的良好效果。此外，针对多租户场景下资源竞争问题，设计了基于博弈论的公平性优化机制，确保不同用户任务获得合理分配。本研究创新性地将机器学习与传统调度理论相结合，在保证性能的同时兼顾了系统的灵活性与鲁棒性，为未来云计算资源管理提供了新的思路和技术支撑。
关键词：云计算资源调度 深度强化学习 负载均衡 能耗控制
Abstract
With the rapid development of cloud computing technology, cloud platforms are confronted with increasingly complex challenges in resource scheduling and load balancing. To enhance resource utilization and service quality, this study focuses on resource scheduling and load balancing strategies in cloud computing environments, aiming to propose an efficient, intelligent, and adaptable solution. By analyzing the shortcomings of existing algorithms in dynamism, scalability, and energy consumption control, a novel scheduling model is constructed using a deep reinforcement learning fr amework. This model can automatically adjust parameters based on real-time workload characteristics, achieving fine-grained management of computing, storage, and network resources. Experimental results demonstrate that the proposed method not only reduces average response time by approximately 30% and increases system throughput by over 25%, but also achieves a significant reduction of 15% in energy consumption. Furthermore, addressing resource contention issues in multi-tenant scenarios, a fairness optimization mechanism based on game theory is designed to ensure reasonable allocation of different user tasks. This research innovatively combines machine learning with traditional scheduling theories, ensuring performance while also considering the flexibility and robustness of the system, thereby providing new insights and technical support for future cloud computing resource management.
Keyword:Cloud computing resource scheduling Deep reinforcement learning Load balancing Energy consumption control
目 录
1 引言 1
2 云计算资源调度模型构建 1
2.1 资源调度的基本概念与原理 1
2.2 调度模型的数学描述 2
2.3 典型调度算法分析比较 3
3 负载均衡策略设计与优化 3
3.1 负载均衡的影响因素 3
3.2 常见负载均衡策略评述 4
3.3 策略优化的目标与方法 4
4 实验验证与结果分析 5
4.1 实验环境与数据集构建 5
4.2 调度与均衡性能测试 6
4.3 结果分析与讨论 6
5 结论 7
参考文献 8
致谢 9
