A multi-level AI-based scheduler to increase adaptiveness in time-constrained mobile communication environments

Scheduling is one of the classic problems in real-time adaptive systems. Due to the complex nature of these applications, the implementation of some sort of run-time intelligence is required, in order to build intelligent systems capable of operating adequately in dynamic environments. The incorporation of artificial intelligence planning techniques in a real-time scenario allows a timely reaction to external and internal events. In this work, a layered architecture integrating real-time scheduling at the bottom level and artificial intelligence planning techniques at the top level has been designed, to implement a multi-level scheduler with the capability to perform effectively in this kind of situation. This multi-level scheduler has been implemented and evaluated in a simulated information access system destined to broadcast information to mobile users in a time-constrained communication environment, modeling mobile users’ realistic information access patterns. Results show that the incorporation of artificial intelligence planning improves the overall performance, adaptiveness, and responsiveness with respect to the non-AI-based scheduler version of the system.