  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.
