摘 要
云计算技术的快速发展使得资源调度效率成为云平台性能的关键。本研究提出了一种基于深度强化学习的自适应资源调度方法，解决传统算法在动态负载下的适应性问题。通过构建多维度资源评估模型，设计了面向服务质量的优化目标函数，并引入改进的深度Q网络算法，提高了学习效率和智能决策能力。实验显示，该方法在任务完成时间上缩短23.7%，资源利用率提升18.5%，系统能耗降低。通过迁移学习，算法适应不同规模云环境的能力增强。本研究创新点包括：深度强化学习在资源调度的应用、基于QoS的多目标优化策略、自学习动态调整机制，以及在线更新优化的调度策略。这些成果对提升云计算性能具有理论和实践价值，为智能云服务发展提供技术支撑。
关键词：深度强化学习；云计算资源调度；自适应优化
OPTIMIZATION OF THE RESOURCE SCHEDULING ALGORITHM IN THE CLOUD COMPUTING ENVIRONMENT
ABSTRACT
The rapid development of cloud computing technology makes the resource scheduling efficiency become the key of the cloud platform performance. This study proposes an adaptive resource scheduling method based on deep reinforcement learning to solve the adaptability of traditional algorithms under dynamic load. By constructing a multi-dimensional resource evaluation model, we have designed the quality-oriented optimization ob jective function of service, and the improved deep Q network algorithm is introduced to improve the learning efficiency and intelligent decision-making ability. The experiment shows that this method shortened the task completion time by 23.7%, increased the resource utilization rate by 18.5%, and reduced the system energy consumption. Through transfer learning, the ability of the algorithm to adapt to cloud environments of different sizes is enhanced. The innovation points of this research include: the application of deep reinforcement learning in resource scheduling, the multi-ob jective optimization strategy based on QoS, the dynamic adjustment mechanism of self-learning, and the scheduling strategy of online update and optimization. These achievements have theoretical and practical value for improving the performance of cloud computing, and provide technical support for the development of intelligent cloud services.
KEY WORDS：Deep reinforcement learning; Cloud computing resource scheduling; Adaptive optimization
目 录
摘 要 I
ABSTRACT II
第1章 绪论 1
1.1 研究背景 1
1.2 研究现状 1
第2章 相关理论概述 2
2.1 云计算资源调度的基本概念 2
2.2 传统资源调度算法的局限性 2
2.3 云计算环境下的调度需求分析 2
第3章 云计算资源调度算法的优化策略 4
3.1 基于负载均衡的调度优化 4
3.2 考虑能耗效率的调度改进 4
3.3 面向服务质量的调度优化方法 5
第4章 云计算资源调度算法的性能评估 6
4.1 实验环境与数据集构建 6
4.2 优化算法的性能指标分析 6
4.3 实验结果与对比分析 7
第5章 结论 8
参考文献 9
致 谢 10
云计算技术的快速发展使得资源调度效率成为云平台性能的关键。本研究提出了一种基于深度强化学习的自适应资源调度方法，解决传统算法在动态负载下的适应性问题。通过构建多维度资源评估模型，设计了面向服务质量的优化目标函数，并引入改进的深度Q网络算法，提高了学习效率和智能决策能力。实验显示，该方法在任务完成时间上缩短23.7%，资源利用率提升18.5%，系统能耗降低。通过迁移学习，算法适应不同规模云环境的能力增强。本研究创新点包括：深度强化学习在资源调度的应用、基于QoS的多目标优化策略、自学习动态调整机制，以及在线更新优化的调度策略。这些成果对提升云计算性能具有理论和实践价值，为智能云服务发展提供技术支撑。
关键词：深度强化学习；云计算资源调度；自适应优化
OPTIMIZATION OF THE RESOURCE SCHEDULING ALGORITHM IN THE CLOUD COMPUTING ENVIRONMENT
ABSTRACT
The rapid development of cloud computing technology makes the resource scheduling efficiency become the key of the cloud platform performance. This study proposes an adaptive resource scheduling method based on deep reinforcement learning to solve the adaptability of traditional algorithms under dynamic load. By constructing a multi-dimensional resource evaluation model, we have designed the quality-oriented optimization ob jective function of service, and the improved deep Q network algorithm is introduced to improve the learning efficiency and intelligent decision-making ability. The experiment shows that this method shortened the task completion time by 23.7%, increased the resource utilization rate by 18.5%, and reduced the system energy consumption. Through transfer learning, the ability of the algorithm to adapt to cloud environments of different sizes is enhanced. The innovation points of this research include: the application of deep reinforcement learning in resource scheduling, the multi-ob jective optimization strategy based on QoS, the dynamic adjustment mechanism of self-learning, and the scheduling strategy of online update and optimization. These achievements have theoretical and practical value for improving the performance of cloud computing, and provide technical support for the development of intelligent cloud services.
KEY WORDS：Deep reinforcement learning; Cloud computing resource scheduling; Adaptive optimization
目 录
摘 要 I
ABSTRACT II
第1章 绪论 1
1.1 研究背景 1
1.2 研究现状 1
第2章 相关理论概述 2
2.1 云计算资源调度的基本概念 2
2.2 传统资源调度算法的局限性 2
2.3 云计算环境下的调度需求分析 2
第3章 云计算资源调度算法的优化策略 4
3.1 基于负载均衡的调度优化 4
3.2 考虑能耗效率的调度改进 4
3.3 面向服务质量的调度优化方法 5
第4章 云计算资源调度算法的性能评估 6
4.1 实验环境与数据集构建 6
4.2 优化算法的性能指标分析 6
4.3 实验结果与对比分析 7
第5章 结论 8
参考文献 9
致 谢 10
