2015 27th Euromicro Conference on Real-Time Systems 2015
DOI: 10.1109/ecrts.2015.9
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Resource Efficient Isolation Mechanisms in Mixed-Criticality Scheduling

Abstract: Mixed-criticality real-time scheduling has been developed to improve resource utilization while guaranteeing safe execution of critical applications. These studies use optimistic resource reservation for all the applications to improve utilization, but prioritize critical applications when the reservations become insufficient at runtime. Many of them however share an impractical assumption that all the critical applications will simultaneously demand additional resources. As a consequence, they under-utilize r… Show more

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Cited by 41 publications
(59 citation statements)
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References 15 publications
(49 reference statements)
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“…Using component-mode, they identify fine-grained execution behaviors of jobs in a component. However, they still have pessimism that all components request up to their HI-criticality budgets when a single component requests its HI-criticality budget [7] or all LO-tasks in a component are dropped at component mode-switch [9]. Our task-mode identifies more fine-grained execution behaviors in the system.…”
Section: Scheduling On Industrial Platformsmentioning
confidence: 99%
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“…Using component-mode, they identify fine-grained execution behaviors of jobs in a component. However, they still have pessimism that all components request up to their HI-criticality budgets when a single component requests its HI-criticality budget [7] or all LO-tasks in a component are dropped at component mode-switch [9]. Our task-mode identifies more fine-grained execution behaviors in the system.…”
Section: Scheduling On Industrial Platformsmentioning
confidence: 99%
“…We schedule 1 RMD is the reverse of deadline miss ratio (DMR): RM D = 1 − DM R 2 If the system mode is HI (LO), current jobs of all tasks may execute up to their HI-WCETs (LO-WCETs). 3 Its example can be found in Gu et al [7]. a LO-task with its real deadline.…”
Section: Scheduling On Industrial Platformsmentioning
confidence: 99%
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