【摘要】 随着6G的发展，云端、边缘端和终端节点间的协作是当下的研究热点，而Map Reduce则是面向大规模数据处理的并行计算模型。将Map Reduce与云-边-端架构相结合，提出基于放置分发阵列的云-边-端协同计算和传输设计架构。该架构充分利用云端和边缘端丰富的计算和存储资源，在边缘端和终端部署冗余计算任务，借助多播编码，成倍地减小云-边链路和边-端链路之间的通信负载，从而实现云-边-端之间通信与计算的协同，高效地服务终端的计算需求。更多还原

【Abstract】 As 6G technology advances, the collaboration among cloud, edge, and end nodes has become a focal point of current research. Map Reduce, a parallel computing model tailored for large-scale data processing, is integral to this domain. This paper presents an architecture that integrates Map Reduce with a cloud-edge-end framework, proposing a design based on placement delivery arrays for collaborative computing and transmission. This architecture capitalizes on the substantial computation and storage resources available at the cloud and edge layers. It deploys redundant computing tasks at the edge and end nodes and utilizes multicast coding to significantly reduce the communication load between cloud-edge and edge-end links. This approach facilitates integrated communication and computing across the cloud, edge, and end layers, efficiently addressing the computational demands of end nodes.更多还原