Pfairness Applied to EDF to Reduce Migration Overheads and Improve Task Schedulability in Multicore Platforms

Abstract

This paper proposes a scheduler combining the concepts of EDF and pfairness using the worst fit heuristic function. In scheduling algorithms without pfairness, priority is not monitored closely in case of preemptions. An algorithm combining EDF and pfairness proposed in this paper overcomes this drawback. Here resources are granted in accordance to the task weight. Individually using either EDF or pfairness utilizes the resources to a greater extent, whereas a combination of both achieves better reduction in migration overheads. The algorithm has been simulated on Cheddar, a real time scheduling tool, and also on SESC, an architectural simulator on multicore platforms. The algorithm presented in this paper has been tested for 5000 random task sets. The results show that it reduces the migration overhead by 33% for partitioned task sets and by 38 % for hybrid task sets, and improves task schedulability by 37%, compared to conventional EDF.