Smart and Adaptive Routing Architecture: An Internet-of-Things Traffic Manager Based on Artificial Neural Networks

Abstract

Many studies have been performed on integrating the Internet of Things (IoT) with cloud services. As these systems become widely used, quality metrics are of concern. For example, users might specify access control to restrict their sensitive data being processed in the cloud. Routers, e.g., API gateways, message brokers, or sidecars, can provide this access control by blocking or routing device data to a specific cloud service. However, a static routing application might not suit the dynamic behavior of IoT applications well. For example, in a centralized schema, where all device data is routed to a component for control checking, performance can be an issue. On the other hand, distributed routing can harm the reliability of a system, as device data might be lost due to an unresponsive service. We present the Smart and Adaptive Routing (SAR) architecture that creates an optimal reconfiguration solution using a deep neural network based on the quality metrics of an IoT application. To design our architecture, we give a background of the published studies and a review of the gray literature, e.g., practitioner blogs, to categorize the knowledge in the domain of IoT-cloud traffic management. We systematically evaluate our approach in an extensive evaluation of 4500 cases and compare SAR with an empirical data set of 1200 hours. The results show that our approach significantly improves quality-of-service measures by adapting the IoT-cloud system at runtime.