云计算环境下资源调度算法的优化与研究
摘 要
随着云计算技术的快速发展，资源调度作为其核心问题之一，直接影响系统的性能与效率。针对传统调度算法在动态复杂环境下的局限性，本文旨在优化云计算环境下的资源调度策略，以提升系统资源利用率和任务响应速度。研究通过引入机器学习模型与自适应控制机制，设计了一种基于预测分析的智能调度算法，该算法能够根据实时负载情况动态调整资源分配策略。实验结果表明，所提方法在多维度性能指标上显著优于现有典型算法，特别是在高并发场景下表现出更优的稳定性和扩展性。本文的主要创新点在于将深度强化学习与传统调度框架相结合，实现了对不确定性的有效应对，并为未来异构云计算环境下的资源管理提供了新思路。研究成果可为实际云计算平台的调度优化提供理论支持和技术参考。
关键词：云计算资源调度；深度强化学习；智能调度算法
Abstract
With the rapid development of cloud computing technology, resource scheduling, as one of its core issues, directly affects the performance and efficiency of the system. In response to the limitations of traditional scheduling algorithms in dynamic and complex environments, this study aims to optimize resource scheduling strategies in cloud computing environments to enhance both system resource utilization and task response speed. By incorporating machine learning models and adaptive control mechanisms, a predictive analytics-based intelligent scheduling algorithm is proposed, which can dynamically adjust resource allocation strategies according to real-time load conditions. Experimental results demonstrate that the proposed method significantly outperforms existing typical algorithms across multiple performance metrics, particularly exhibiting superior stability and scalability in high-concurrency scenarios. The primary innovation of this study lies in the integration of deep reinforcement learning with traditional scheduling fr ameworks, enabling effective handling of uncertainties and providing new insights for resource management in future heterogeneous cloud computing environments. The research findings offer theoretical support and technical references for optimizing scheduling in practical cloud computing platforms.
Keywords: Cloud Computing Resource Scheduling；Deep Reinforcement Learning；Intelligent Scheduling Algorithm
目 录
引言 1
一、云计算资源调度基础研究 1
（一）云计算环境概述 1
（二）资源调度基本原理 1
（三）当前调度算法挑战 2
二、调度算法优化理论分析 2
（一）优化目标与约束条件 2
（二）常见优化方法综述 3
（三）算法性能评估指标 3
三、动态负载下的调度策略改进 4
（一）动态负载特性分析 4
（二）自适应调度机制设计 4
（三）实时性与效率平衡 4
四、分布式环境中的调度实现 5
（一）分布式架构适配研究 5
（二）跨区域资源调度优化 5
（三）故障容忍与可靠性提升 6
结 论 6
致 谢 8
参考文献 9
摘 要
随着云计算技术的快速发展，资源调度作为其核心问题之一，直接影响系统的性能与效率。针对传统调度算法在动态复杂环境下的局限性，本文旨在优化云计算环境下的资源调度策略，以提升系统资源利用率和任务响应速度。研究通过引入机器学习模型与自适应控制机制，设计了一种基于预测分析的智能调度算法，该算法能够根据实时负载情况动态调整资源分配策略。实验结果表明，所提方法在多维度性能指标上显著优于现有典型算法，特别是在高并发场景下表现出更优的稳定性和扩展性。本文的主要创新点在于将深度强化学习与传统调度框架相结合，实现了对不确定性的有效应对，并为未来异构云计算环境下的资源管理提供了新思路。研究成果可为实际云计算平台的调度优化提供理论支持和技术参考。
关键词：云计算资源调度；深度强化学习；智能调度算法
Abstract
With the rapid development of cloud computing technology, resource scheduling, as one of its core issues, directly affects the performance and efficiency of the system. In response to the limitations of traditional scheduling algorithms in dynamic and complex environments, this study aims to optimize resource scheduling strategies in cloud computing environments to enhance both system resource utilization and task response speed. By incorporating machine learning models and adaptive control mechanisms, a predictive analytics-based intelligent scheduling algorithm is proposed, which can dynamically adjust resource allocation strategies according to real-time load conditions. Experimental results demonstrate that the proposed method significantly outperforms existing typical algorithms across multiple performance metrics, particularly exhibiting superior stability and scalability in high-concurrency scenarios. The primary innovation of this study lies in the integration of deep reinforcement learning with traditional scheduling fr ameworks, enabling effective handling of uncertainties and providing new insights for resource management in future heterogeneous cloud computing environments. The research findings offer theoretical support and technical references for optimizing scheduling in practical cloud computing platforms.
Keywords: Cloud Computing Resource Scheduling；Deep Reinforcement Learning；Intelligent Scheduling Algorithm
目 录
引言 1
一、云计算资源调度基础研究 1
（一）云计算环境概述 1
（二）资源调度基本原理 1
（三）当前调度算法挑战 2
二、调度算法优化理论分析 2
（一）优化目标与约束条件 2
（二）常见优化方法综述 3
（三）算法性能评估指标 3
三、动态负载下的调度策略改进 4
（一）动态负载特性分析 4
（二）自适应调度机制设计 4
（三）实时性与效率平衡 4
四、分布式环境中的调度实现 5
（一）分布式架构适配研究 5
（二）跨区域资源调度优化 5
（三）故障容忍与可靠性提升 6
结 论 6
致 谢 8
参考文献 9
