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Multiprocessor Scheduling of Mixed-Criticality Parallel Systems
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.
Liu, Guangdong, "Multiprocessor Scheduling of Mixed-Criticality Parallel Systems" (2017). ETD collection for University of Nebraska - Lincoln. AAI10683150.