Partitioned multiprocessor scheduling of mixed-criticality parallel jobs
Guangdong Liu, Ying Lü, Shige Wang, Zonghua Gu · 2014
Motivated by the increasing trend in embedded systems towards platform integration, there has been an increasing research interest in scheduling mixed-criticality systems. However, most existing efforts have concentrated on scheduling sequential tasks and ignored intra-task parallelism. In this paper, we study the scheduling of mixed-criticality parallel jobs on multiprocessor platforms. We propose a synchronous mixed-criticality job model, where each job consists of segments, each segment having an arbitrary number of parallel threads that synchronize at the end of the segment. A novel MinLoad algorithm is developed to decompose mixed-criticality parallel jobs into mixed-criticality sequential jobs. This decomposition enables us to leverage existing mixed-criticality scheduling algorithms and schedulability analysis to the multiprocessor scheduling of mixed-criticality parallel jobs. In addition, our MinLoad job decomposition algorithm is designed to make the decomposed mixed-criticality sequential tasks easier to schedule, and thus requires smaller-sized multiprocessor platforms for the mixed-criticality systems.