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

AI Back-End as a Service for Learning Switching of Mobile Apps between the Fog and the Cloud