Task reweighting under global scheduling on multiprocessors

We consider schemes for enacting task share changes - a process called reweighting - on real-time multiprocessor platforms. Our particular focus is reweighting schemes that are deployed in environments in which tasks may frequently request significant share changes. Prior work has shown that fair scheduling algorithms are capable of reweighting tasks with minimal allocation error and that partitioning-based scheduling algorithms can reweight tasks with better average-case performance, but greater error. However, preemption and migration overheads can be high in fair schemes. In this paper, we consider the question of whether global scheduling techniques can improve the accuracy of reweighting relative to partitioning-based schemes and provide improved average-case performance relative to fair-scheduled systems. Our conclusion is that, for soft real-time systems, global scheduling techniques provide a good mix of accuracy and average-case performance