As deep learning, virtual reality, and other technologies become mature, real-time data processing applications running on intelligent terminals are emerging endlessly; meanwhile, edge computing has developed rapidly and has become a popular research direction in the field of distributed computing. Edge computing network is a network computing environment composed of multi-edge computing nodes and data centers. First, the edge computing framework and key technologies are analyzed to improve the performance of real-time data processing applications. In the system scenario where the collaborative deployment tasks of multi-edge nodes and data centers are considered, the stream processing task deployment process is formally described, and an efficient multi-edge node-computing center collaborative task deployment algorithm is proposed, which solves the problem of copy-free task deployment in the task deployment problem. Furthermore, a heterogeneous edge collaborative storage mechanism with tight coupling of computing and data is proposed, which solves the contradiction between the limited computing and storage capabilities of data and intelligent terminals, thereby improving the performance of data processing applications. Here, a Feasible Solution (FS) algorithm is designed to solve the problem of placing copy-free data processing tasks in the system. The FS algorithm has excellent results once considering the overall coordination. Under light load, the V value is reduced by 73% compared to the Only Data Center-available (ODC) algorithm and 41% compared to the Hash algorithm. Under heavy load, the V value is reduced by 66% compared to the ODC algorithm and 35% compared to the Hash algorithm. The algorithm has achieved good results after considering the overall coordination and cooperation and can more effectively use the bandwidth of edge nodes to transmit and process data stream, so that more tasks can be deployed in edge computing nodes, thereby saving time for data transmission to the data centers. The end-to-end collaborative real-time data processing task scheduling mechanism proposed here can effectively avoid the disadvantages of long waiting times and unable to obtain the required data, which significantly improves the success rate of the task and thus ensures the performance of real-time data processing.

随着深度学习、虚拟现实等技术的成熟，运行在智能终端上的实时数据处理应用层出不穷；与此同时，边缘计算发展迅速，成为分布式计算领域的热门研究方向。边缘计算网络是由多边缘计算节点和数据中心组成的网络计算环境。首先，分析边缘计算框架和关键技术，以提高实时数据处理应用程序的性能。在考虑多边缘节点与数据中心协同部署任务的系统场景下，正式描述流处理任务部署过程，提出一种高效的多边缘节点-计算中心协同任务部署算法，解决了任务部署问题中的无副本任务部署问题。此外，提出了一种计算与数据紧耦合的异构边缘协同存储机制，解决了数据与智能终端计算与存储能力有限的矛盾，从而提升了数据处理应用的性能。在这里，设计了一个可行的解决方案（FS）算法来解决在系统中放置无副本数据处理任务的问题。FS 算法在考虑整体协调性时有很好的效果。轻负载下，V值比ODC算法降低73%，比Hash算法降低41%。在沉重的负荷下，V值比ODC算法降低66%，比Hash算法降低35%。该算法在考虑整体协调配合后取得了较好的效果，能够更有效地利用边缘节点的带宽来传输和处理数据流，从而可以在边缘计算节点部署更多的任务，从而节省数据传输到边缘节点的时间。数据中心。这里提出的端到端协同实时数据处理任务调度机制，可以有效避免等待时间长、无法获取所需数据的缺点，显着提高了任务的成功率，从而保证了实时数据处理的性能。 - 时间数据处理。该算法在考虑整体协调配合后取得了较好的效果，能够更有效地利用边缘节点的带宽来传输和处理数据流，从而可以在边缘计算节点部署更多的任务，从而节省数据传输到边缘节点的时间。数据中心。这里提出的端到端协同实时数据处理任务调度机制，可以有效避免等待时间长、无法获取所需数据的缺点，显着提高了任务的成功率，从而保证了实时数据处理的性能。 - 时间数据处理。该算法在考虑整体协调配合后取得了较好的效果，能够更有效地利用边缘节点的带宽来传输和处理数据流，从而可以在边缘计算节点部署更多的任务，从而节省数据传输到边缘节点的时间。数据中心。这里提出的端到端协同实时数据处理任务调度机制，可以有效避免等待时间长、无法获取所需数据的缺点，显着提高了任务的成功率，从而保证了实时数据处理的性能。 - 时间数据处理。