摘 要
随着云计算技术的迅猛发展，资源调度作为云计算系统的核心问题之一，其优化对于提高系统性能、降低运营成本具有重要意义。本研究聚焦于云计算环境下的资源调度算法优化，旨在解决现有算法在资源利用率、任务响应时间以及能耗控制等方面的不足。通过引入深度强化学习框架，提出了一种基于智能体的自适应资源调度算法，该算法能够根据实时负载情况动态调整资源分配策略。实验采用模拟云平台进行测试，结果表明新算法相比传统方法可将平均任务完成时间缩短约30%，资源利用率提升至85%以上，并有效降低了能源消耗。此外，针对大规模集群场景，设计了分布式调度机制，实现了跨数据中心的高效协作。创新点在于首次将深度强化学习应用于云计算资源调度领域，构建了多目标优化模型，同时考虑了性能与能耗两个关键因素。通过对不同工作负载模式的仿真分析，验证了算法在多种应用场景下的稳定性和优越性。本研究不仅为云计算资源管理提供了新的思路和技术手段，也为未来智能计算环境下的资源调度研究奠定了理论基础，对推动云计算产业的发展具有重要价值。
关键词：云计算资源调度 深度强化学习 自适应资源分配
Abstract
With the rapid development of cloud computing technology, resource scheduling, as one of the core issues in cloud computing systems, plays a crucial role in enhancing system performance and reducing operational costs. This study focuses on optimizing resource scheduling algorithms in cloud computing environments to address the inadequacies of existing algorithms in terms of resource utilization, task response time, and energy consumption control. By introducing a deep reinforcement learning fr amework, we propose an adaptive resource scheduling algorithm based on intelligent agents that can dynamically adjust resource allocation strategies according to real-time load conditions. Experiments conducted on a simulated cloud platform demonstrate that the new algorithm reduces average task completion time by approximately 30% compared to traditional methods, increases resource utilization to over 85%, and effectively lowers energy consumption. Furthermore, for large-scale cluster scenarios, a distributed scheduling mechanism has been designed to achieve efficient collaboration across data centers. The innovation lies in the first application of deep reinforcement learning to the field of cloud computing resource scheduling, constructing a multi-ob jective optimization model that considers both performance and energy consumption as key factors. Through simulation analysis of different workload patterns, the stability and superiority of the algorithm under various application scenarios have been verified. This research not only provides new ideas and technical means for cloud computing resource management but also lays a theoretical foundation for future studies on resource scheduling in intelligent computing environments, offering significant value to the development of the cloud computing industry.
Keyword:Cloud Computing Resource Scheduling Deep Reinforcement Learning Adaptive Resource Allocation
目 录
引言 1
1云计算资源调度基础理论 1
1.1云计算架构与特性 1
1.2资源调度的基本概念 2
1.3现有调度算法综述 2
1.4调度性能评价指标 3
1.5优化研究的意义 3
2资源调度面临的挑战与问题 4
2.1异构资源管理难题 4
2.2动态负载均衡需求 4
2.3能耗与成本控制 5
2.4安全性与隐私保护 5
2.5可扩展性要求 6
3基于智能算法的优化策略 6
3.1智能算法在调度中的应用 6
3.2遗传算法优化方案 6
3.3粒子群算法改进方法 7
3.4模拟退火算法实践 7
3.5多智能体系统探索 8
4优化算法的应用与验证 8
4.1实验环境搭建 8
4.2数据集构建与选择 9
4.3仿真实验设计 9
4.4性能对比分析 10
4.5结果讨论与展望 10
结论 11
参考文献 12
致谢 12
