Learning-Oriented QoS- and Drop-Aware Task Scheduling for Mixed-Criticality Systems

Abstract

In Mixed-Criticality (MC) systems, multiple functions with different levels of criticality are integrated into a common platform in order to meet the intended space, cost, and timing requirements in all criticality levels. To guarantee the correct, and on-time execution of higher criticality tasks in emergency modes, various design-time scheduling policies have been recently presented. These techniques are mostly pessimistic, as the occurrence of worst-case scenario at run-time is a rare event. Nevertheless, they lead to an under-utilized system due to frequent drops of Low-Criticality (LC) tasks, and creation of unused slack times due to the quick execution of high-criticality tasks. Accordingly, this paper proposes a novel optimistic scheme, that introduces a learning-based drop-aware task scheduling mechanism, which carefully monitors the alterations in the behaviour of the MC system at run-time, to exploit the generated dynamic slacks for reducing the LC tasks penalty and preventing frequent drops of LC tasks in the future. Based on an extensive set of experiments, our observations have shown that the proposed approach exploits accumulated dynamic slack generated at run-time, by 9.84% more on average compared to existing works, and is able to reduce the deadline miss rate by up to 51.78%, and 33.27% on average, compared to state-of-the-art works.

Keywords

• drop rate
• dynamic slack
• Machine-Learning
• Mixed-Criticality
• Quality-of-Service (QoS)
• run-time management