AI Back-End as a Service for Learning Switching of Mobile Apps between the Fog and the Cloud
This addresses latency issues for mobile app users by optimizing resource use between edge and cloud, though it is incremental as it builds on existing back-end as a service frameworks.
The paper tackles the problem of delays in mobile apps by dynamically switching between fog and cloud back-end instances to minimize response time, achieving improved accuracy and efficiency in a real-world auction app evaluation.
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.