Safe Online Reconfiguration of Mixed-Criticality Real-Time Systems

Kadeed, Thawra; Nikolic, Borislav; Ernst, Rolf

doi:10.1109/prdc50213.2020.00025

Cited by 1 publication

(1 citation statement)

References 22 publications

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“…ATMP-CA differs from all the works described above in that it does not restrict the source of resource shortages in WCET estimate overruns, and it is capable of considering information about criticality arithmetic. Kadeed et al introduced online reconfiguration mechanisms to be used in case of foreseen core failures, while ensuring sufficient isolation according to safety standards between critical and non-critical services during the reconfiguration process [17]. The mechanism assumes continuous monitoring for service degradation is available, and identifies a foreseen core failure if services degradation exceed the hazardous level.…”

Section: Related Workmentioning

confidence: 99%

ATMP-CA: Optimising Mixed-Criticality Systems Considering Criticality Arithmetic

2021

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Many safety-critical systems use criticality arithmetic, an informal practice of implementing a higher-criticality function by combining several lower-criticality redundant components or tasks. This lowers the cost of development, but existing mixed-criticality schedulers may act incorrectly as they lack the knowledge that the lower-criticality tasks are operating together to implement a single higher-criticality function. In this paper, we propose a solution to this problem by presenting a mixed-criticality mid-term scheduler that considers where criticality arithmetic is used in the system. As this scheduler, which we term ATMP-CA, is a mid-term scheduler, it changes the configuration of the system when needed based on the recent history of deadline misses. We present the results from a series of experiments that show that ATMP-CA’s operation provides a smoother degradation of service compared with reference schedulers that do not consider the use of criticality arithmetic.

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Section: Related Workmentioning

confidence: 99%