2014
DOI: 10.1016/j.microrel.2014.02.004
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Evaluation of Level of Confidence and Optimization of Roll-back Recovery with Checkpointing for Real-Time Systems

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Cited by 6 publications
(3 citation statements)
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“…Focus was on the dynamic retargeting process, memory requirements, hierarchical module isolation, propagation of test coverage results. We also develop mathematical frameworks where we studied Rollback Recovery with Check-pointing under various assumptions, such as multiple jobs [NIK15a], [NIK15b], [NIK16].…”
Section: Fault Management Methodology and Field Learningmentioning
confidence: 99%
“…Focus was on the dynamic retargeting process, memory requirements, hierarchical module isolation, propagation of test coverage results. We also develop mathematical frameworks where we studied Rollback Recovery with Check-pointing under various assumptions, such as multiple jobs [NIK15a], [NIK15b], [NIK16].…”
Section: Fault Management Methodology and Field Learningmentioning
confidence: 99%
“…However, the value M should be decided to guarantee that the length of the resource supply cannot violate the deadline requirement of the faulty task, and that the total additional resource supplied for backup is large enough to cover the backup requirement of each task in case of fault. These two conditions are then formalized into Equation (14).…”
Section: Interface Generationmentioning
confidence: 99%
“…Such a framework should provide an efficient resource model for an effective resource sharing even in presence of faults. Nevertheless, many error recovery strategies such as redundancy [8,12,15], roll-back [7,14,20] and roll-forward [13,19] with check-pointing [5], have already been devised for the long studied field of fault-tolerance in real-time systems, but their direct application to a compositional scheduling framework has not been thoroughly investigated.…”
Section: Introductionmentioning
confidence: 99%