PT-AMC: integrating preemption thresholds into mixed-criticality scheduling
Proceedings of the Conference on Design, Automation and Test in Europe
Fixed-priority scheduling of dual-criticality systems
Proceedings of the 21st International conference on Real-Time Networks and Systems
Two protocols to reduce the criticality level of multiprocessor mixed-criticality systems
Proceedings of the 21st International conference on Real-Time Networks and Systems
Scheduling of mixed-criticality applications on resource-sharing multicore systems
Proceedings of the Eleventh ACM International Conference on Embedded Software
Implementation and evaluation of mixed-criticality scheduling approaches for sporadic tasks
ACM Transactions on Embedded Computing Systems (TECS)
Mixed-criticality scheduling on multiprocessors
Real-Time Systems
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An increasing trend in embedded system design is to integrate components with different levels of criticality into a shared hardware platform for better cost and power efficiency. Such mixed-criticality systems are subject to certifications at different levels of rigorousness, for validating the correctness of different subsystems on various confidence levels. The real-time scheduling of certifiable mixed-criticality systems has been recognized to be a challenging problem, where using traditional scheduling techniques may result in unacceptable resource waste. In this paper we present an algorithm called PLRS to schedule certifiable mixed-criticality sporadic tasks systems. PLRS uses fixed-job-priority scheduling, and assigns job priorities by exploring and balancing the asymmetric effects between the workload on different criticality levels. Comparing with the state-of-the-art algorithm by Li and Baruah for such systems, which we refer to as LB, PLRS is both more effective and more efficient: (i) The schedulability test of PLRS not only theoretically dominates, but also on average significantly outperforms LB's. (ii) The run-time complexity of PLRS is polynomial (quadratic in the number of tasks), which is much more efficient than the pseudo-polynomial run-time complexity of LB.