To alleviate the huge computing pressure caused by the single mobile edge server computing mode as the amount of data increases, in this research, we propose a method to conduct calculations in a collaborative way. First, the method needs to consider how to encourage devices to cooperate when they are selfish. Second, the method answers the following question: how can collaborative computing be carried out when the device has the intention to cooperate? For example, how can calculations be conducted when there are extensibility and privacy problems in machine learning tasks? In view of the above challenges, a mobile edge server is taken as the focus, and the available resources around the mobile edge server are used for collaborative computing to further improve the computing performance of a mobile edge computing (MEC) system. The alternating direction multiplier method is used to solve the problem. First, the relevant techniques and theories of MEC, Stackelberg principle-subordinate game theory, and the alternating direction method of multipliers (ADMM) are introduced. Then, the problem description and model construction of distributed task scheduling in MEC and machine learning task-based device coordination computing are introduced, and machine learning is applied in the distributed task scheduling algorithm and distributed device coordination algorithm. Finally, the distributed task scheduling algorithm and distributed device coordination algorithm are tested by experiments.

为了缓解单一移动边缘服务器计算模式随数据量增加而带来的巨大计算压力，在本研究中，我们提出了一种以协作方式进行计算的方法。首先，该方法需要考虑在设备自私时如何鼓励它们合作。其次，该方法回答了以下问题：当设备有协作意图时，如何进行协作计算？例如，当机器学习任务中存在可扩展性和隐私问题时，如何进行计算？鉴于上述挑战，以移动边缘服务器为重点，并将移动边缘服务器周围的可用资源用于协同计算，以进一步提高移动边缘计算（MEC）系统的计算性能。使用交变方向乘数法可以解决该问题。首先，介绍了MEC的相关技术和理论，Stackelberg原理-从属博弈论以及乘数的交替方向法（ADMM）。然后，介绍了MEC中分布式任务调度的问题描述和模型构建以及基于机器学习任务的设备协调计算，并将机器学习应用于分布式任务调度算法和分布式设备协调算法。最后，通过实验测试了分布式任务调度算法和分布式设备协调算法。介绍了乘法器的交替方向法（ADMM）。然后，介绍了MEC中分布式任务调度的问题描述和模型构建以及基于机器学习任务的设备协调计算，并将机器学习应用于分布式任务调度算法和分布式设备协调算法。最后，通过实验测试了分布式任务调度算法和分布式设备协调算法。介绍了乘法器的交替方向法（ADMM）。然后，介绍了MEC中分布式任务调度的问题描述和模型构建以及基于机器学习任务的设备协调计算，并将机器学习应用于分布式任务调度算法和分布式设备协调算法。最后，通过实验测试了分布式任务调度算法和分布式设备协调算法。