Supporting Delay-Sensitive IoT Applications: A Machine Learning Approach

Abstract

In this paper, a supervised machine learning approach, namely, the decision tree is used to classify IoT applications based on their delay requirements. The decision-tree is trained and tested using simulated datasets to classify tasks into delay-sensitive and delay-insensitive based on the application features such as type and location. Delay-sensitive tasks are generally related to applications such as medical, manufacturing, and connected vehicles that require high service quality and short response time. Once delay-sensitive tasks are recognized, a prioritized scheduling mechanism is implemented to reduce the queueing delay at edge devices. Here, a two-class priority queueing system is used to model the scheduling mechanism at the edge device. Results show the effectiveness of machine learning in identifying delay-sensitive tasks that will experience shorter queueing delay at the edge device to enable high quality edge computing services.