Edge computing architectures are designed for providing in-network computational and resource allocation support for the connected users. Such architectures provide ease of computations and latency-less resource allocations for improving the efficiency of service disseminations. The problem of multi-access to the resources increases the latency in distributing the computing features for concurrent users. In this article, access to appropriate analogous computing (A3C) is proposed for addressing this issue. The proposed computing model performs the parallel request and migration processing for providing multiple accesses to the available edge resources. The multiple processing instances are identified based on the service dissemination and lag to the migrating resource factors. The instances are differentiated, and supportive migration access is analyzed for the two factors using transfer learning. This learning helps to improve the recommendations for distributing the resources for additive access. The proposed computing model improves the ratio of service dissemination under controlled latency and service lag. The stagnancy in distributing the service computations using the edge devices helps provide reliable access to edge users. The experimental analysis shows that the proposed A3C improves service dissemination by 11.52%, computation distribution by 8.6%, and access rate by 10.03%, whereas it reduces the service lag and latency by 22.2% and 11.07%, respectively, for the different edge devices.

边缘计算体系结构旨在为连接的用户提供网络内计算和资源分配支持。这样的体系结构提供了易于计算和无等待时间的资源分配，以提高服务分发的效率。对资源的多路访问问题增加了为并发用户分配计算功能的等待时间。在本文中，提出了访问适当的模拟计算（A3C）的方法来解决此问题。所提出的计算模型执行并行请求和迁移处理，以提供对可用边缘资源的多次访问。基于服务分发和滞后于迁移资源因素来标识多个处理实例。实例是有区别的，并使用转移学习分析了支持性迁移访问的两个因素。这种学习有助于改进有关分配资源以进行增补访问的建议。所提出的计算模型提高了在受控等待时间和服务滞后下的服务分发比率。使用边缘设备来分布服务计算的停滞有助于提供对边缘用户的可靠访问。实验分析表明，提出的A3C可以将服务分发提高11.52％，将计算分布提高8.6％，将访问率提高10.03％，而对于不同的边缘设备，则分别将服务延迟和延迟降低22.2％和11.07％ 。所提出的计算模型提高了在受控等待时间和服务滞后下的服务分发比率。使用边缘设备来分布服务计算的停滞有助于提供对边缘用户的可靠访问。实验分析表明，提出的A3C可以将服务分发提高11.52％，将计算分布提高8.6％，将访问率提高10.03％，而对于不同的边缘设备，则分别将服务延迟和延迟降低22.2％和11.07％ 。所提出的计算模型提高了在受控等待时间和服务滞后下的服务分发比率。使用边缘设备来分布服务计算的停滞有助于提供对边缘用户的可靠访问。实验分析表明，提出的A3C可以将服务分发提高11.52％，将计算分布提高8.6％，将访问率提高10.03％，而对于不同的边缘设备，则分别将服务延迟和延迟降低22.2％和11.07％ 。