On-Line Optimization Techniques for Dynamic Scheduling of Real-Time Tasks
Yacine Atif, Babak Hamidzadeh · Industrial and Engineering Applications of Artificial Intelligence and Expert Systems · 2022
In this paper, we explore the application of a new class of on-line optimization techniques, referred to as Self-Adjusting Real-Time Search (SARTS), to dynamic scheduling of sporadic real-time tasks on a uniprocessor architecture. The task model selected is that of non-preemptable tasks with arbitrary start times and deadlines. The selected search algorithms address a fundamental trade-off between the cost of the scheduling process and the quality of the delivered solution.