摘 要
随着云计算技术的迅猛发展，资源调度作为云计算平台高效运行的关键环节，其优化研究具有重要意义。本研究旨在针对云计算平台下资源调度算法进行深入探讨，以实现资源利用率的最大化和任务响应时间的最小化为目标。通过分析现有资源调度算法存在的资源分配不均、任务等待时间长等问题，提出一种基于深度强化学习的自适应资源调度算法。该算法利用深度强化学习强大的环境感知与决策能力，根据云平台实时负载情况动态调整资源分配策略。实验结果表明，相较于传统算法，所提算法能够有效降低平均任务完成时间约30%，提高资源利用率约25%。此外，该算法还展现出良好的可扩展性，在大规模云计算环境中依然保持稳定性能。这一创新性算法为解决云计算平台资源调度难题提供了新思路，不仅有助于提升云计算服务的质量与效率，也为相关领域的研究提供了有益参考。
关键词：云计算资源调度；深度强化学习；自适应算法；任务响应时间；资源利用率
Abstract
With the rapid development of cloud computing technology, resource scheduling, as a critical component for the efficient operation of cloud computing platforms, has become an important area of optimization research. This study aims to thoroughly investigate resource scheduling algorithms in cloud computing environments with the ob jective of maximizing resource utilization and minimizing task response time. By analyzing existing resource scheduling algorithms, which suffer from issues such as uneven resource distribution and long task waiting times, we propose an adaptive resource scheduling algorithm based on deep reinforcement learning. This algorithm leverages the powerful environmental perception and decision-making capabilities of deep reinforcement learning to dynamically adjust resource allocation strategies according to the real-time load conditions of the cloud platform. Experimental results demonstrate that, compared to traditional algorithms, the proposed algorithm effectively reduces average task completion time by approximately 30% and increases resource utilization by about 25%. Additionally, the algorithm exhibits excellent scalability, maintaining stable performance in large-scale cloud computing environments. This innovative algorithm provides new insights into solving resource scheduling challenges in cloud computing platforms, enhancing the quality and efficiency of cloud computing services, and offering valuable references for research in related fields.
Keywords：Cloud Computing Resource Scheduling；Deep Reinforcement Learning；Adaptive Algorithm；Task Response Time；Resource Utilization
目 录
摘 要 I
Abstract II
引 言 1
第一章 云计算资源调度基础理论 2
1.1 云计算平台架构分析 2
1.2 资源调度的基本概念 2
1.3 主流调度算法综述 3
第二章 资源调度需求与挑战 5
2.1 弹性计算需求分析 5
2.2 多租户环境下的挑战 5
2.3 性能与成本的平衡 6
第三章 典型资源调度算法研究 8
3.1 静态调度算法剖析 8
3.2 动态调度算法特点 8
3.3 混合调度算法设计 9
第四章 调度算法优化与应用 11
4.1 基于机器学习的优化 11
4.2 实时任务调度策略 11
4.3 行业应用场景分析 12
结 论 13
参考文献 14
致 谢 15
