Given that cloud servers are usually remotely located from the devices of mobile apps, the end-users of the apps can face delays. The Fog has been introduced to augment the apps with machines located at the network edge close to the end-users. However, edge machines are usually resource constrained. Thus, the execution of online data-analytics on edge machines may not be feasible if the time complexity of the data-analytics algorithm is high. To overcome this, multiple instances of the back-end should be deployed on edge and remote machines. In this case, the research question is how the switching of the app among the instances of the back-end can be dynamically decided based on the response time of the service instances. To answer this, we contribute an AI approach that trains machine-learning models of the response time of service instances. Our approach extends a back-end as a service into an AI self-back-end as a service that self-decides at runtime the right edge/remote instance that achieves the lowest response-time. We evaluate the accuracy and the efficiency of our approach by using real-word machine-learning datasets on an existing auction app.

中文翻译：

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AI 后端即服务，用于学习在雾和云之间切换移动应用程序

鉴于云服务器通常远离移动应用程序的设备，应用程序的最终用户可能会面临延迟。引入 Fog 是为了通过位于靠近最终用户的网络边缘的机器来增强应用程序。然而，边缘机器通常是资源受限的。因此，如果数据分析算法的时间复杂度很高，在边缘机器上执行在线数据分析可能是不可行的。为了克服这个问题，应该在边缘和远程机器上部署多个后端实例。在这种情况下，研究的问题是如何根据服务实例的响应时间动态决定应用程序在后端实例之间的切换。为了回答这个问题，我们提供了一种人工智能方法，可以训练服务实例响应时间的机器学习模型。我们的方法将后端即服务扩展为 AI 自我后端即服务，该服务在运行时自行决定实现最低响应时间的正确边缘/远程实例。我们通过在现有拍卖应用程序上使用真实的机器学习数据集来评估我们方法的准确性和效率。