Heterogeneous Quasi-Partitioned Scheduling

We consider the problem of scheduling a set of preemptible independent periodic implicit-deadline hard real-time tasks on heterogeneous processors. We divide this problem into two sub-problems: (a) assigning portions of each processor (offline) to each task without jeopardizing schedulability; and (b) generating a schedule satisfying the assigned portions using an online semi-partitioned scheduler, called Heterogeneous Quasi-Partitioned Scheduling (hQPS). The scheduler handles task servers at run-time for ensuring that the processor shares assigned to tasks are timely available to them. Assessments indicate that the proposed solution (i) has good scalability (up to 64 tasks, 64 processors), (ii) is effective in generating schedules with few preemptions and few migrations, and (iii) is effective in managing resources; for task sets where an extra processor speed is required, our solution needs at most 10% extra compared to an optimal scheduler.