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

TL;DR

This paper presents an AI-driven system that dynamically switches mobile app back-end instances between fog and cloud to minimize response times, addressing latency issues caused by resource constraints and network delays.

Contribution

It introduces a machine learning-based approach for real-time decision-making in back-end instance switching, extending back-end as a service into an AI self-managed system.

Findings

High accuracy in response time prediction

Effective reduction in app latency

Feasible real-time deployment

Abstract

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.