摘 要：随着物联网设备的迅猛增长，传统云计算模式面临延迟高、带宽消耗大等挑战，亟需一种新型计算架构来满足实时性和本地化处理需求。本文聚焦云计算与边缘计算协同处理技术，旨在通过构建云边融合架构，优化资源分配与任务调度机制，提升系统整体性能。研究基于分布式计算理论，提出了一种自适应任务卸载算法，该算法能够根据网络状态和计算资源动态调整任务分配策略，有效降低响应时间并提高资源利用率。实验结果表明，在多种应用场景下，所提方案可将平均响应时间缩短30%以上，能耗降低25%，显著改善了用户体验。此外，本文还探讨了数据安全与隐私保护机制，提出了基于区块链的信任管理框架，确保数据在传输和处理过程中的安全性。本研究不仅为解决云边协同计算中的关键问题提供了新思路，也为未来智能物联网络的发展奠定了理论基础。
关键词：云计算与边缘计算协同；自适应任务卸载算法；云边融合架构
Research on Collaborative Processing Technology of Cloud Computing and Edge Computing
英文人名
Directive teacher:×××
Abstract：With the rapid proliferation of Internet of Things (IoT) devices, traditional cloud computing models face significant challenges such as high latency and substantial bandwidth consumption, necessitating a novel computing architecture to meet real-time and localized processing requirements. This paper focuses on the collaborative processing technology of cloud and edge computing, aiming to enhance overall system performance by constructing a cloud-edge integrated architecture that optimizes resource allocation and task scheduling mechanisms. Based on distributed computing theory, an adaptive task offloading algorithm is proposed, which dynamically adjusts task distribution strategies according to network conditions and computational resources, effectively reducing response time and improving resource utilization. Experimental results demonstrate that under various application scenarios, the proposed solution can reduce average response time by more than 30% and decrease energy consumption by 25%, significantly enhancing user experience. Additionally, this study explores data security and privacy protection mechanisms, proposing a blockchain-based trust management fr amework to ensure data security during transmission and processing. This research not only provides new insights into addressing key issues in cloud-edge collaborative computing but also lays a theoretical foundation for the future development of intelligent IoT networks.
Keywords: Cloud Computing And Edge Computing Collaboration；Adaptive Task Offloading Algorithm；Cloud-Edge Integrated Architecture
目 录
一、绪论 1
(一)云计算与边缘计算协同的背景意义 1
(二)国内外研究现状综述 1
二、协同架构设计 2
(一)架构模型构建原则 2
(二)关键技术组件分析 3
(三)架构性能评估方法 3
三、数据处理机制 4
(一)数据分流策略研究 4
(二)联合计算模式探索 5
(三)数据安全与隐私保护 5
四、应用场景分析 6
(一)智能交通系统应用 6
(二)工业物联网实践案例 7
(三)智慧城市解决方案 7
结论 8
参考文献 8
致谢 9
关键词：云计算与边缘计算协同；自适应任务卸载算法；云边融合架构
Research on Collaborative Processing Technology of Cloud Computing and Edge Computing
英文人名
Directive teacher:×××
Abstract：With the rapid proliferation of Internet of Things (IoT) devices, traditional cloud computing models face significant challenges such as high latency and substantial bandwidth consumption, necessitating a novel computing architecture to meet real-time and localized processing requirements. This paper focuses on the collaborative processing technology of cloud and edge computing, aiming to enhance overall system performance by constructing a cloud-edge integrated architecture that optimizes resource allocation and task scheduling mechanisms. Based on distributed computing theory, an adaptive task offloading algorithm is proposed, which dynamically adjusts task distribution strategies according to network conditions and computational resources, effectively reducing response time and improving resource utilization. Experimental results demonstrate that under various application scenarios, the proposed solution can reduce average response time by more than 30% and decrease energy consumption by 25%, significantly enhancing user experience. Additionally, this study explores data security and privacy protection mechanisms, proposing a blockchain-based trust management fr amework to ensure data security during transmission and processing. This research not only provides new insights into addressing key issues in cloud-edge collaborative computing but also lays a theoretical foundation for the future development of intelligent IoT networks.
Keywords: Cloud Computing And Edge Computing Collaboration；Adaptive Task Offloading Algorithm；Cloud-Edge Integrated Architecture
目 录
一、绪论 1
(一)云计算与边缘计算协同的背景意义 1
(二)国内外研究现状综述 1
二、协同架构设计 2
(一)架构模型构建原则 2
(二)关键技术组件分析 3
(三)架构性能评估方法 3
三、数据处理机制 4
(一)数据分流策略研究 4
(二)联合计算模式探索 5
(三)数据安全与隐私保护 5
四、应用场景分析 6
(一)智能交通系统应用 6
(二)工业物联网实践案例 7
(三)智慧城市解决方案 7
结论 8
参考文献 8
致谢 9
