Fixed Priority Global Scheduling from a Deep Learning Perspective

TL;DR

This paper explores how deep learning techniques can be applied to fixed priority global scheduling problems in real-time systems, offering potential improvements in scheduling quality across complex scenarios.

Contribution

It presents an initial approach to using deep learning for fixed priority global scheduling and discusses potential generalizations for more complex scheduling scenarios.

Findings

Deep learning can be adapted for real-time task scheduling.

Potential for improved scheduling in complex scenarios.

Framework for future research in DL-based scheduling.

Abstract

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.