分布式系统中的异构资源管理与调度
摘 要
随着信息技术的迅猛发展，分布式系统在云计算、大数据处理等领域得到广泛应用，其核心挑战之一在于如何高效管理与调度异构资源。本文聚焦于分布式系统中异构资源管理与调度问题，旨在构建一种适应性强、效率高的资源管理与调度框架。针对传统调度算法难以兼顾资源异构性与任务多样性的问题，提出基于深度强化学习的自适应调度算法，该算法通过引入多维特征表示模型，实现对计算、存储、网络等不同类型资源的精准评估。实验结果表明，在多种典型应用场景下，所提算法能够有效降低平均任务完成时间约25%，提高资源利用率18%以上。此外，设计了面向异构环境的资源抽象层，支持动态感知与配置调整，增强了系统的灵活性与可扩展性。
关键词：分布式系统 异构资源管理 深度强化学习
Abstract
With the rapid development of information technology, distributed systems have been widely used in cloud computing, big data processing and other fields. One of the core challenges is how to efficiently manage and schedule heterogeneous resources. This paper focuses on the heterogeneous resource management and scheduling problems in distributed systems, aiming to build a resource management and scheduling fr amework with strong adaptability and high efficiency. In view of the problem that it is difficult to balance resource heterogeneity and task diversity of traditional scheduling algorithm, an adaptive scheduling algorithm based on deep reinforcement learning is proposed. By introducing multi-dimensional feature representation model, the algorithm realizes accurate evaluation of different types of resources such as computing, storage and network. The experimental results show that the proposed algorithm can effectively reduce the average task completion time by about 25% and improve the resource utilization rate by more than 18% under various typical application scenarios. In addition, the resource abstraction layer oriented to the heterogeneous environment is designed to support the dynamic perception and configuration adjustment, which enhances the flexibility and scalability of the system.
Keyword:Distributed System Heterogeneous Resource Management Deep Reinforcement Learning
目 录
1绪论 1
1.1分布式系统异构资源管理背景 1
1.2研究意义与价值分析 1
1.3国内外研究现状综述 1
1.4本文研究方法概述 2
2异构资源特性分析 2
2.1资源类型多样性解析 2
2.2资源性能差异评估 3
2.3资源可用性影响因素 3
3调度策略设计与优化 4
3.1调度目标设定原则 4
3.2常见调度算法比较 5
3.3自适应调度机制构建 5
4管理框架与实现技术 6
4.1框架架构设计思路 6
4.2关键技术应用分析 7
4.3实施效果与挑战应对 7
结论 8
参考文献 9
致谢 10
摘 要
随着信息技术的迅猛发展，分布式系统在云计算、大数据处理等领域得到广泛应用，其核心挑战之一在于如何高效管理与调度异构资源。本文聚焦于分布式系统中异构资源管理与调度问题，旨在构建一种适应性强、效率高的资源管理与调度框架。针对传统调度算法难以兼顾资源异构性与任务多样性的问题，提出基于深度强化学习的自适应调度算法，该算法通过引入多维特征表示模型，实现对计算、存储、网络等不同类型资源的精准评估。实验结果表明，在多种典型应用场景下，所提算法能够有效降低平均任务完成时间约25%，提高资源利用率18%以上。此外，设计了面向异构环境的资源抽象层，支持动态感知与配置调整，增强了系统的灵活性与可扩展性。
关键词：分布式系统 异构资源管理 深度强化学习
Abstract
With the rapid development of information technology, distributed systems have been widely used in cloud computing, big data processing and other fields. One of the core challenges is how to efficiently manage and schedule heterogeneous resources. This paper focuses on the heterogeneous resource management and scheduling problems in distributed systems, aiming to build a resource management and scheduling fr amework with strong adaptability and high efficiency. In view of the problem that it is difficult to balance resource heterogeneity and task diversity of traditional scheduling algorithm, an adaptive scheduling algorithm based on deep reinforcement learning is proposed. By introducing multi-dimensional feature representation model, the algorithm realizes accurate evaluation of different types of resources such as computing, storage and network. The experimental results show that the proposed algorithm can effectively reduce the average task completion time by about 25% and improve the resource utilization rate by more than 18% under various typical application scenarios. In addition, the resource abstraction layer oriented to the heterogeneous environment is designed to support the dynamic perception and configuration adjustment, which enhances the flexibility and scalability of the system.
Keyword:Distributed System Heterogeneous Resource Management Deep Reinforcement Learning
目 录
1绪论 1
1.1分布式系统异构资源管理背景 1
1.2研究意义与价值分析 1
1.3国内外研究现状综述 1
1.4本文研究方法概述 2
2异构资源特性分析 2
2.1资源类型多样性解析 2
2.2资源性能差异评估 3
2.3资源可用性影响因素 3
3调度策略设计与优化 4
3.1调度目标设定原则 4
3.2常见调度算法比较 5
3.3自适应调度机制构建 5
4管理框架与实现技术 6
4.1框架架构设计思路 6
4.2关键技术应用分析 7
4.3实施效果与挑战应对 7
结论 8
参考文献 9
致谢 10
