摘 要
随着云计算技术的快速发展，资源调度效率成为影响云平台性能的关键因素。本研究针对传统调度算法在动态负载环境下适应性不足的问题，提出了一种基于深度强化学习的自适应资源调度算法。该算法通过构建多维度资源评估模型，综合考虑任务特征、节点负载和网络状况等因素，实现了对异构资源的精准评估与分配。研究采用改进的深度Q网络框架，设计了兼顾公平性与效率的奖励函数，并通过引入经验回放机制和双重Q学习策略提升了算法的收敛速度和稳定性。实验结果表明，与传统轮询调度、最小负载优先等算法相比，所提算法在任务完成时间、资源利用率和系统吞吐量等关键指标上均有显著提升，其中平均任务响应时间降低约23.5%，资源利用率提高18.7%。此外，算法展现出良好的动态适应能力，能够有效应对突发性负载波动。本研究的主要创新点在于将深度强化学习与云计算资源调度相结合，突破了传统启发式算法的局限性，为复杂云环境下的智能调度提供了新的解决方案。研究成果对提升云计算平台的运行效率和用户体验具有重要的理论价值和实践意义。
关键词：深度强化学习 云计算资源调度 自适应算法
Abstract
With the rapid development of cloud computing technology, resource scheduling efficiency has become a key factor affecting the performance of cloud platform. This study addresses the insufficient adaptability of the traditional scheduling algorithm in a dynamic load environment, and proposes an adaptive resource scheduling algorithm based on deep reinforcement learning. By constructing a multi-dimensional resource evaluation model, and comprehensively considering the task characteristics, node load and network status, the algorithm realizes the accurate evaluation and allocation of heterogeneous resources. The improved deep Q network fr amework is used to design the reward function that combines fairness and efficiency, and then the convergence speed and stability of the algorithm are improved by introducing empirical playback mechanism and dual Q learning strategy. The experimental results show that compared with the traditional algorithms such as polling scheduling and minimum load priority, the proposed algorithm has a significant improvement in key indicators such as task completion time, resource utilization and system throughput, in which the average task response time decreases by about 23.5% and the resource utilization increases by 18.7%. In addition, the algorithm shows a good dynamic adaptation ability, and can effectively respond to the sudden load fluctuations. The main innovation of this study is the combination of deep reinforcement learning and cloud computing resource scheduling, which breaks through the limitations of traditional heuristic algorithms and provides a new solution for intelligent scheduling in complex cloud environments. The research results have important theoretical value and practical significance for improving the operation efficiency and user experience of the cloud computing platform.
Keyword: Deep reinforcement learning Cloud computing resource scheduling Adaptive algorithm
目 录
1 引言 1
2 云计算资源调度模型构建 1
2.1 云计算平台资源特性分析 1
2.2 资源调度问题建模方法 2
2.3 典型调度模型比较分析 3
3 云计算资源调度算法设计 3
3.1 基于负载预测的调度策略 3
3.2 多目标优化调度算法设计 4
3.3 动态自适应调度机制实现 5
4 云计算资源调度算法性能评估 5
4.1 实验环境与测试数据集构建 5
4.2 算法性能评价指标体系 6
4.3 实验结果分析与对比讨论 7
5 结论 7
参考文献 9
致谢 10
随着云计算技术的快速发展，资源调度效率成为影响云平台性能的关键因素。本研究针对传统调度算法在动态负载环境下适应性不足的问题，提出了一种基于深度强化学习的自适应资源调度算法。该算法通过构建多维度资源评估模型，综合考虑任务特征、节点负载和网络状况等因素，实现了对异构资源的精准评估与分配。研究采用改进的深度Q网络框架，设计了兼顾公平性与效率的奖励函数，并通过引入经验回放机制和双重Q学习策略提升了算法的收敛速度和稳定性。实验结果表明，与传统轮询调度、最小负载优先等算法相比，所提算法在任务完成时间、资源利用率和系统吞吐量等关键指标上均有显著提升，其中平均任务响应时间降低约23.5%，资源利用率提高18.7%。此外，算法展现出良好的动态适应能力，能够有效应对突发性负载波动。本研究的主要创新点在于将深度强化学习与云计算资源调度相结合，突破了传统启发式算法的局限性，为复杂云环境下的智能调度提供了新的解决方案。研究成果对提升云计算平台的运行效率和用户体验具有重要的理论价值和实践意义。
关键词：深度强化学习 云计算资源调度 自适应算法
Abstract
With the rapid development of cloud computing technology, resource scheduling efficiency has become a key factor affecting the performance of cloud platform. This study addresses the insufficient adaptability of the traditional scheduling algorithm in a dynamic load environment, and proposes an adaptive resource scheduling algorithm based on deep reinforcement learning. By constructing a multi-dimensional resource evaluation model, and comprehensively considering the task characteristics, node load and network status, the algorithm realizes the accurate evaluation and allocation of heterogeneous resources. The improved deep Q network fr amework is used to design the reward function that combines fairness and efficiency, and then the convergence speed and stability of the algorithm are improved by introducing empirical playback mechanism and dual Q learning strategy. The experimental results show that compared with the traditional algorithms such as polling scheduling and minimum load priority, the proposed algorithm has a significant improvement in key indicators such as task completion time, resource utilization and system throughput, in which the average task response time decreases by about 23.5% and the resource utilization increases by 18.7%. In addition, the algorithm shows a good dynamic adaptation ability, and can effectively respond to the sudden load fluctuations. The main innovation of this study is the combination of deep reinforcement learning and cloud computing resource scheduling, which breaks through the limitations of traditional heuristic algorithms and provides a new solution for intelligent scheduling in complex cloud environments. The research results have important theoretical value and practical significance for improving the operation efficiency and user experience of the cloud computing platform.
Keyword: Deep reinforcement learning Cloud computing resource scheduling Adaptive algorithm
目 录
1 引言 1
2 云计算资源调度模型构建 1
2.1 云计算平台资源特性分析 1
2.2 资源调度问题建模方法 2
2.3 典型调度模型比较分析 3
3 云计算资源调度算法设计 3
3.1 基于负载预测的调度策略 3
3.2 多目标优化调度算法设计 4
3.3 动态自适应调度机制实现 5
4 云计算资源调度算法性能评估 5
4.1 实验环境与测试数据集构建 5
4.2 算法性能评价指标体系 6
4.3 实验结果分析与对比讨论 7
5 结论 7
参考文献 9
致谢 10
