Fixed Priority Global Scheduling from a Deep Learning Perspective
This work addresses the challenge of real-time task scheduling for system designers by proposing Deep Learning as a solution, though the current contribution is preliminary.
This paper explores the application of Deep Learning to fixed priority global scheduling (FPGS) problems, a type of combinatorial optimization. It presents preliminary work on adopting Deep Learning for FPGS and discusses potential generalizations for more complex scenarios like tasks with dependencies and mixed-criticality scheduling.
Deep Learning has been recently recognized as one of the feasible solutions to effectively address combinatorial optimization problems, which are often considered important yet challenging in various research domains. In this work, we first present how to adopt Deep Learning for real-time task scheduling through our preliminary work upon fixed priority global scheduling (FPGS) problems. We then briefly discuss possible generalizations of Deep Learning adoption for several realistic and complicated FPGS scenarios, e.g., scheduling tasks with dependency, mixed-criticality task scheduling. We believe that there are many opportunities for leveraging advanced Deep Learning technologies to improve the quality of scheduling in various system configurations and problem scenarios.