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Multiprocessor Scheduling of Mixed-Criticality Parallel Systems

Guangdong Liu, University of Nebraska - Lincoln

Abstract

Motivated by the increasing trend in embedded systems towards platform integration, there has been an increasing research interest in scheduling mixed-criticality (MC) systems. However, most existing efforts have concentrated on scheduling sequential mixed-criticality tasks and ignored intra-task parallelism. As MC systems are increasingly being implemented on multiprocessor platforms, it is important to take advantage of intra-task parallelism in order to accommodate tasks with higher execution demands and tighter deadlines, such as those used in autonomous vehicles, video processing, radar tracking and robotic systems. ^ To fill in this research gap, we conduct a systematic study on multiprocessor scheduling of MC parallel tasks. At first, we studied the scheduling of parallel nonrecurring MC jobs, which is considered as a first step towards a more comprehensive study of scheduling recurrent parallel MC tasks. We then investigate different approaches to scheduling MC parallel systems on multiprocessors. In particular, four different multiprocessor scheduling algorithms are developed: 1) Partitioned Multiprocessor Scheduling of MC Parallel Jobs; 2) Global Scheduling of Parallel MC Tasks without Task Decomposition; 3) Partitioned Scheduling of MC Parallel Tasks; 4) Decomposition-Based Global Scheduling of MC Parallel Tasks with Deadline Tuning. Simulation experiments are conducted to evaluate these scheduling algorithms. The experimental results confirm the effectiveness of the proposed scheduling algorithms.^

Subject Area

Computer science

Recommended Citation

Liu, Guangdong, "Multiprocessor Scheduling of Mixed-Criticality Parallel Systems" (2017). ETD collection for University of Nebraska - Lincoln. AAI10683150.
https://digitalcommons.unl.edu/dissertations/AAI10683150

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