IMC-PALM: Enhancing Survivability of Imprecise Mixed-Criticality Cyber-Physical Systems via Mode-Balanced Partitioning and Adaptive Task Migration

초록

Complex cyber-physical systems in the automotive and avionics domains increasingly consolidate safety-critical and non-critical functions onto shared multicore platforms. A central design challenge in these environments is ensuring strict timing guarantees while allowing graceful degradation under resource contention. In partitioned multiprocessor mixed-criticality systems, a mode switch on one processor forces all low-criticality (LC) tasks on that processor to degrade to their mandatory execution budgets, even though neighboring processors may have spare capacity. Existing approaches either focus on uniprocessor systems or address only the offline partitioning problem without considering the runtime survivability of LC tasks. This paper proposes IMC-PALM (Imprecise Mixed-Criticality via Partitioning and Adaptive Lightweight Migration), a two-phase framework for imprecise mixed-criticality (IMC) multiprocessor systems. The offline phase combines a tightened EDF-VD-IMC schedulability test with Mode-Balanced Partitioning (MBP). The tightened test identifies high-criticality tasks whose HI-mode utilization yields a tighter bound, while MBP allocates tasks based on mode-specific residual capacity. The runtime phase migrates LC tasks from a mode-switched processor to other processors that remain in LO mode, exploiting the per-processor isolation property of partitioned scheduling. Simulation results with 2, 4, and 8 processors show that MBP with the tightened test improves the acceptance ratio by 12.3 %p over existing algorithms under standard conditions. Furthermore, runtime migration reduces the degraded job ratio by 28.1 %p compared to the no-migration baseline under these standard settings, of which 4.6 %p is attributable to home-processor recovery. The benefit grows with the number of processors and remains robust under realistic migration overheads.

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