Abstract—Scheduling task graphs under hard (end-to-end) timing constraints is an extensively studied NP-hard problem of critical importance for predictable software mapping on Multiprocessor System-on-chip (MPSoC) platforms. In this work we focus on an off-line (design-time) version of this problem, where the target task graph is known before execution time. We address the issue of scheduling robustness, i.e. providing hard guarantees that the schedule will meet the end-to-end deadline in presence of bounded variations of task execution times expressed as min-max intervals known at design time. We present a robust scheduling algorithm that proactively inserts sequencing constraints when they are needed to ensure that execution will have no inserted idle times and will meet the deadline for any possible combination of task execution times within the specified intervals. The algorithm is complete, i.e. it will return a feasible graph augmentation if one exists. Moreover, we provide an optimization version of the algorithm that can compute the shortest deadline that can be met in a robust way.

Robust non-preemptive hard real-time scheduling for clustered multicore platforms

LOMBARDI, MICHELE;MILANO, MICHELA;BENINI, LUCA
2009

Abstract

Abstract—Scheduling task graphs under hard (end-to-end) timing constraints is an extensively studied NP-hard problem of critical importance for predictable software mapping on Multiprocessor System-on-chip (MPSoC) platforms. In this work we focus on an off-line (design-time) version of this problem, where the target task graph is known before execution time. We address the issue of scheduling robustness, i.e. providing hard guarantees that the schedule will meet the end-to-end deadline in presence of bounded variations of task execution times expressed as min-max intervals known at design time. We present a robust scheduling algorithm that proactively inserts sequencing constraints when they are needed to ensure that execution will have no inserted idle times and will meet the deadline for any possible combination of task execution times within the specified intervals. The algorithm is complete, i.e. it will return a feasible graph augmentation if one exists. Moreover, we provide an optimization version of the algorithm that can compute the shortest deadline that can be met in a robust way.
Design, Automation and Test in Europe, DATE 2009
803
808
M. Lombardi; M. Milano; L. Benini
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/81244
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