面向人工智能边缘计算的架构设计与优化
摘 要
随着物联网和智能设备的广泛应用，边缘计算作为连接物理世界与数字世界的桥梁，在人工智能应用中发挥着至关重要的作用。本文聚焦于面向人工智能的边缘计算架构设计与优化问题，旨在构建一种高效、低延迟且适应性强的边缘计算架构。针对现有架构在资源分配、任务调度及数据传输等方面的不足，提出了一种基于层次化资源管理与自适应任务调度机制的新型架构。该架构通过引入多级缓存机制有效降低了数据传输延迟，并利用深度强化学习算法实现了动态资源分配优化。实验结果表明，所提出的架构能够显著提高系统响应速度，降低能耗，提升整体性能达30%以上。特别是在处理大规模实时数据流场景下，其优势更加明显。
关键词：边缘计算架构 人工智能 资源分配优化
Abstract
With the wide application of the Internet of Things and intelligent devices, edge computing plays a vital role in the application of artificial intelligence as a bridge connecting the physical world and the digital world. This paper focuses on the design and optimization of edge computing architecture for artificial intelligence, aiming to build an efficient, low-latency and highly adaptable edge computing architecture. Aiming at the shortcomings of the existing architecture in resource allocation, task scheduling and data transmission, a new architecture based on hierarchical resource management and adaptive task scheduling mechanism is proposed. The architecture effectively reduces the data transmission delay by introducing the multi-level caching mechanism, and realizes the dynamic resource allocation optimization with the deep reinforcement learning algorithm. The experimental results show that the proposed architecture can significantly improve the system response speed, reduce energy consumption, and improve the overall performance by more than 30%. Especially in the processing of large-scale real-time data flow scene, its advantages are more obvious.
Keyword:Edge Computing Architecture Artificial Intelligence Resource Allocation Optimization
目 录
1绪论 1
1.1研究背景与意义 1
1.2国内外研究现状 1
1.3研究方法与技术路线 2
2边缘计算架构设计原则 2
2.1架构需求分析 2
2.2关键技术选择 3
2.3设计目标与约束 3
2.4架构模型构建 4
3人工智能算法优化策略 4
3.1算法适配性分析 4
3.2模型压缩与加速 5
3.3资源分配优化 5
3.4性能评估体系 6
4系统性能评估与改进 6
4.1测试环境搭建 7
4.2性能指标定义 7
4.3实验结果分析 8
4.4改进措施探讨 8
结论 9
参考文献 10
致谢 11
摘 要
随着物联网和智能设备的广泛应用，边缘计算作为连接物理世界与数字世界的桥梁，在人工智能应用中发挥着至关重要的作用。本文聚焦于面向人工智能的边缘计算架构设计与优化问题，旨在构建一种高效、低延迟且适应性强的边缘计算架构。针对现有架构在资源分配、任务调度及数据传输等方面的不足，提出了一种基于层次化资源管理与自适应任务调度机制的新型架构。该架构通过引入多级缓存机制有效降低了数据传输延迟，并利用深度强化学习算法实现了动态资源分配优化。实验结果表明，所提出的架构能够显著提高系统响应速度，降低能耗，提升整体性能达30%以上。特别是在处理大规模实时数据流场景下，其优势更加明显。
关键词：边缘计算架构 人工智能 资源分配优化
Abstract
With the wide application of the Internet of Things and intelligent devices, edge computing plays a vital role in the application of artificial intelligence as a bridge connecting the physical world and the digital world. This paper focuses on the design and optimization of edge computing architecture for artificial intelligence, aiming to build an efficient, low-latency and highly adaptable edge computing architecture. Aiming at the shortcomings of the existing architecture in resource allocation, task scheduling and data transmission, a new architecture based on hierarchical resource management and adaptive task scheduling mechanism is proposed. The architecture effectively reduces the data transmission delay by introducing the multi-level caching mechanism, and realizes the dynamic resource allocation optimization with the deep reinforcement learning algorithm. The experimental results show that the proposed architecture can significantly improve the system response speed, reduce energy consumption, and improve the overall performance by more than 30%. Especially in the processing of large-scale real-time data flow scene, its advantages are more obvious.
Keyword:Edge Computing Architecture Artificial Intelligence Resource Allocation Optimization
目 录
1绪论 1
1.1研究背景与意义 1
1.2国内外研究现状 1
1.3研究方法与技术路线 2
2边缘计算架构设计原则 2
2.1架构需求分析 2
2.2关键技术选择 3
2.3设计目标与约束 3
2.4架构模型构建 4
3人工智能算法优化策略 4
3.1算法适配性分析 4
3.2模型压缩与加速 5
3.3资源分配优化 5
3.4性能评估体系 6
4系统性能评估与改进 6
4.1测试环境搭建 7
4.2性能指标定义 7
4.3实验结果分析 8
4.4改进措施探讨 8
结论 9
参考文献 10
致谢 11
