The Partitioned, Static-Priority Scheduling of Sporadic Real-Time Tasks with Constrained Deadlines on Multiprocessor Platforms

We consider the partitioned scheduling of sporadic, hard-realtime tasks on a multiprocessor platform with static-priority scheduling policies. Most previous work on the static-priority scheduling of sporadic tasks upon multiprocessors has assumed implicit deadlines (i.e. a task’s relative deadline is equal to its period). We relax the equality constraint on a task’s deadline and consider task systems with constrained deadlines (i.e. relative deadlines are at most periods). In particular, we consider the first-fit decreasing partitioning algorithm. Since the partitioning problem is easily seen to be NP-hard in the strong sense, this algorithm is unlikely to be optimal. We quantitatively characterize the partitioning algorithm’s worst-case performance in terms of resource augmentation.