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算法，以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状态特征空间，并利用深度Q网络实现最优决策，该算法有效提升了传统启发式算法在复杂任务中的全局最优解获取能力。在CloudSim仿真平台上的实验验证了算法在不同负载场景下的性能，结果显示，与传统算法相比，新算法在任务完成时间、资源利用率和服务质量等方面有显著提升，平均任务响应时间降低约23.6%，资源利用率提高15.8%。算法还展示了良好的可扩展性和鲁棒性，适用于大规模分布式环境。本研究的创新在于将深度强化学习应用于云计算资源调度，为智能决策提供了新方案，对提升云计算平台运营效率和服务质量具有理论和实践意义，为智能云资源管理系统设计提供了参考。
关键词：深度强化学习 云计算资源调度 自适应算法 深度Q网络
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 algorithm based on deep reinforcement learning to solve the adaptability of traditional algorithms under dynamic load. By constructing the multi-dimensional resource state feature space and using the deep Q network to realize the optimal decision, the algorithm effectively improves the global optimal solution acquisition ability of the traditional heuristic algorithm in complex tasks. The experiment on the CloudSim simulation platform verifies the performance of the algorithm in different load scenarios. The results show that compared with the traditional algorithm, the new algorithm has significantly improved in terms of task completion time, resource utilization and service quality, with the average task response time reduced by about 23.6%, and the resource utilization increased by 15.8%. The algorithm also demonstrates good scalability and robustness, suitable for large-scale distributed environments. The innovation of this research lies in the application of deep reinforcement learning to cloud computing resource scheduling, which provides a new solution for intelligent decision-making. It has theoretical and practical significance for improving the operational efficiency and service quality of cloud computing platform, and provides a reference for the design of intelligent cloud resource management system.
Keywords: Deep reinforcement learning Cloud computing resource scheduling Adaptive algorithm Deep Q network
目 录
1 引言 1
2 云计算资源调度算法概述 1
2.1 云计算资源调度的基本概念 1
2.2 资源调度算法的分类与特点 1
2.3 云计算环境对资源调度的影响 2
3 云计算资源调度算法的关键技术 2
3.1 任务分配策略的优化方法 2
3.2 负载均衡机制的设计与实现 3
3.3 能耗优化在资源调度中的应用 3
4 典型云计算资源调度算法分析 4
4.1 基于遗传算法的资源调度模型 4
4.2 蚁群算法在云资源调度中的改进 4
4.3 深度学习驱动的智能调度方法 5
5 云计算资源调度算法的性能评估 5
5.1 评估指标体系的构建原则 5
5.2 仿真实验平台的设计与实现 6
5.3 算法性能对比与分析结果 6
6 结论 6
致 谢 8
参考文献 9
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算法，以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状态特征空间，并利用深度Q网络实现最优决策，该算法有效提升了传统启发式算法在复杂任务中的全局最优解获取能力。在CloudSim仿真平台上的实验验证了算法在不同负载场景下的性能，结果显示，与传统算法相比，新算法在任务完成时间、资源利用率和服务质量等方面有显著提升，平均任务响应时间降低约23.6%，资源利用率提高15.8%。算法还展示了良好的可扩展性和鲁棒性，适用于大规模分布式环境。本研究的创新在于将深度强化学习应用于云计算资源调度，为智能决策提供了新方案，对提升云计算平台运营效率和服务质量具有理论和实践意义，为智能云资源管理系统设计提供了参考。
关键词：深度强化学习 云计算资源调度 自适应算法 深度Q网络
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 algorithm based on deep reinforcement learning to solve the adaptability of traditional algorithms under dynamic load. By constructing the multi-dimensional resource state feature space and using the deep Q network to realize the optimal decision, the algorithm effectively improves the global optimal solution acquisition ability of the traditional heuristic algorithm in complex tasks. The experiment on the CloudSim simulation platform verifies the performance of the algorithm in different load scenarios. The results show that compared with the traditional algorithm, the new algorithm has significantly improved in terms of task completion time, resource utilization and service quality, with the average task response time reduced by about 23.6%, and the resource utilization increased by 15.8%. The algorithm also demonstrates good scalability and robustness, suitable for large-scale distributed environments. The innovation of this research lies in the application of deep reinforcement learning to cloud computing resource scheduling, which provides a new solution for intelligent decision-making. It has theoretical and practical significance for improving the operational efficiency and service quality of cloud computing platform, and provides a reference for the design of intelligent cloud resource management system.
Keywords: Deep reinforcement learning Cloud computing resource scheduling Adaptive algorithm Deep Q network
目 录
1 引言 1
2 云计算资源调度算法概述 1
2.1 云计算资源调度的基本概念 1
2.2 资源调度算法的分类与特点 1
2.3 云计算环境对资源调度的影响 2
3 云计算资源调度算法的关键技术 2
3.1 任务分配策略的优化方法 2
3.2 负载均衡机制的设计与实现 3
3.3 能耗优化在资源调度中的应用 3
4 典型云计算资源调度算法分析 4
4.1 基于遗传算法的资源调度模型 4
4.2 蚁群算法在云资源调度中的改进 4
4.3 深度学习驱动的智能调度方法 5
5 云计算资源调度算法的性能评估 5
5.1 评估指标体系的构建原则 5
5.2 仿真实验平台的设计与实现 6
5.3 算法性能对比与分析结果 6
6 结论 6
致 谢 8
参考文献 9
