As a mainstream computing and storage strategy for mobile communications, Internet of Things and large data applications, mobile edge computing strategy mainly benefits from the deployment and allocation of small base stations. Mobile edge computing mainly helps users to complete complex, intensive and sensitive computing tasks. However, the algorithm has many problems in practical application, such as complex user needs, complex user mobility, numerous services and applications. Therefore, under the above background, it is of great significance to solve the computational pressure of current mobile edge algorithm and optimize its algorithm architecture. This paper creatively proposes a deep learning architecture based on tightly connected network, and transplants it into mobile edge algorithm to realize the payload sharing process of edge computing, so as to establish an efficient network model. At the same time, we creatively propose a multi-task parallel scheduling algorithm, which realizes the mobile edge algorithm in the face of complex computing and algorithm efficiency. Finally, the above algorithms are simulated and tested. The experimental results show that under the same task, the time consumed by the proposed algorithm is 3.5–4, while the time consumed by the traditional algorithm is 4.5–8, and the corresponding time is standardized time, so the practice shows that the algorithm has obvious overall efficiency advantages.

作为移动通信，物联网和大数据应用程序的主流计算和存储策略，移动边缘计算策略主要受益于小型基站的部署和分配。移动边缘计算主要帮助用户完成复杂，密集和敏感的计算任务。然而，该算法在实际应用中存在许多问题，例如复杂的用户需求，复杂的用户移动性，众多的服务和应用。因此，在上述背景下，解决当前移动边缘算法的计算压力并优化其算法架构具有重要意义。本文创造性地提出了一种基于紧密连接网络的深度学习架构，并将其移植到移动边缘算法中，以实现边缘计算的有效载荷共享过程，从而建立有效的网络模型。同时，我们创造性地提出了一种多任务并行调度算法，该算法在面对复杂计算和算法效率的情况下实现了移动边缘算法。最后，对上述算法进行了仿真和测试。实验结果表明，在相同任务下，该算法消耗的时间为3.5–4，而传统算法消耗的时间为4.5–8，并且对应的时间为标准时间，因此实践表明该算法具有明显的整体效率优势。面对复杂的计算和算法效率，实现了移动边缘算法。最后，对上述算法进行了仿真和测试。实验结果表明，在相同任务下，该算法消耗的时间为3.5–4，而传统算法消耗的时间为4.5–8，并且对应的时间为标准时间，因此实践表明该算法具有明显的整体效率优势。面对复杂的计算和算法效率，实现了移动边缘算法。最后，对上述算法进行了仿真和测试。实验结果表明，在相同任务下，该算法消耗的时间为3.5–4，而传统算法消耗的时间为4.5–8，并且对应的时间为标准时间，因此实践表明该算法具有明显的整体效率优势。