摘 要
随着云计算技术的迅猛发展，资源调度作为云计算系统的核心问题之一，其研究对于提高资源利用率、降低运营成本和提升服务质量具有重要意义。本研究聚焦于云计算环境下的资源调度算法，旨在解决现有算法在多租户场景下资源分配不均、响应时间长以及能耗过高等问题。为此，提出了一种基于深度强化学习的自适应资源调度算法，该算法通过构建虚拟机与物理机之间的映射模型，利用深度神经网络对历史数据进行学习，并结合强化学习机制动态调整资源分配策略。实验结果表明，所提算法能够有效减少平均任务完成时间约30%，提高资源利用率25%以上，同时降低能耗达20%。此外，该算法还具备良好的可扩展性和鲁棒性，在不同规模的云计算环境中均能保持稳定性能。相较于传统调度算法，本研究的最大创新点在于将深度学习与强化学习相结合应用于资源调度领域，不仅实现了更精准的任务预测和资源匹配，而且为未来智能调度系统的开发提供了新的思路和技术支持。
关键词：云计算资源调度 深度强化学习 自适应算法
Abstract
With the rapid development of cloud computing technology, resource scheduling, as one of the core issues in cloud computing systems, plays a crucial role in improving resource utilization, reducing operational costs, and enhancing service quality. This study focuses on resource scheduling algorithms in cloud computing environments, aiming to address the problems of uneven resource distribution, long response times, and high energy consumption in multi-tenant scenarios. To this end, a deep reinforcement learning-based adaptive resource scheduling algorithm is proposed. This algorithm establishes a mapping model between virtual machines and physical machines, leveraging deep neural networks to learn from historical data and incorporating reinforcement learning mechanisms to dynamically adjust resource allocation strategies. Experimental results demonstrate that the proposed algorithm can effectively reduce average task completion time by approximately 30%, increase resource utilization by over 25%, and decrease energy consumption by up to 20%. Additionally, the algorithm exhibits excellent scalability and robustness, maintaining stable performance across cloud computing environments of varying scales. Compared with traditional scheduling algorithms, the most significant innovation of this research lies in combining deep learning with reinforcement learning for application in resource scheduling, achieving more accurate task prediction and resource matching while providing new insights and technical support for the development of future intelligent scheduling systems.
Keyword:Cloud computing resource scheduling Deep reinforcement learning Adaptive algorithm
目 录
1 引言 1
2 云计算资源调度需求分析 1
2.1 云计算环境特征分析 1
2.2 资源调度的关键需求 2
2.3 用户需求与服务质量要求 2
3 现有资源调度算法评估 3
3.1 常见调度算法原理 3
3.2 算法性能对比分析 4
3.3 现有算法的局限性 4
4 新型资源调度算法设计 5
4.1 设计目标与原则 5
4.2 关键技术与创新点 5
4.3 算法模型构建 6
5 结论 7
参考文献 8
致谢 9
