Cloud-edge collaboration architecture, which combines edge processing and centralized cloud processing, is suitable for placement and caching of streaming media. A cache-aware scheduling model based on neighborhood search is proposed. The model is divided into four sub-problems: job classification, node resource allocation, node clustering, and cache-aware job scheduling. Firstly, jobs are categorized into three categories, and then different resources are allocated to nodes according to different job execution conditions. Secondly, the nodes with similar capabilities are clustered, and the jobs are cached by delay-waiting. For jobs that do not satisfy the data locality, the jobs are scheduled to the nodes with similar capabilities according to the neighborhood search results. Meanwhile, a cache-aware scheduling algorithm based on neighborhood search is proposed. Experiments show that the proposed algorithm can effectively minimize the delay of content transmission and the cost of content placement, the job execution time is shortened and the processing capacity of the cloud data center is improved.

中文翻译：

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基于邻域搜索的分布式边缘云计算环境下物联网大数据实时处理作业调度

云边协同架构，结合边缘处理和集中式云处理，适用于流媒体的放置和缓存。提出了一种基于邻域搜索的缓存感知调度模型。该模型分为四个子问题：作业分类、节点资源分配、节点聚类和缓存感知作业调度。首先将作业分为三类，然后根据不同的作业执行情况为节点分配不同的资源。其次，将能力相近的节点聚集在一起，通过延迟等待的方式缓存作业。对于不满足数据局部性的作业，根据邻域搜索结果将作业调度到能力相近的节点。同时，提出了一种基于邻域搜索的缓存感知调度算法。实验表明，所提算法能够有效降低内容传输延迟和内容投放成本，缩短作业执行时间，提高云数据中心的处理能力。