Efficient and Flexible Fair Scheduling of Real-time Tasks on Multiprocessors

Anand Srinivasan Efficient and Flexible Fair Scheduling of Real-time Tasks on Multiprocessors (Under the direction of James H. Anderson) Proportionate fair (Pfair) scheduling is the only known way to optimally schedule periodic real-time task systems on multiprocessors in an on-line manner. Under Pfair scheduling, the execution of each task is broken into a sequence of quantum-length subtasks that must execute within intervals of approximately-equal lengths. This scheduling policy results in allocations that mimic those of an ideal “fluid” scheduler, and in periodic task systems, ensures that all deadlines are met. Though Pfair scheduling algorithms hold much promise, prior to our work, research on this topic was limited in that only static systems consisting of synchronous periodic tasks were considered. My dissertation thesis is that the Pfair scheduling framework for the on-line scheduling of real-time tasks on multiprocessors can be made more flexible by allowing the underlying task model to be more general than the periodic model and by allowing dynamic task behaviors. Further, this flexibility can be efficiently achieved . Towards the goal of improving the efficiency of Pfair scheduling algorithms, we develop the PD Pfair algorithm, which is the most efficient optimal Pfair scheduling algorithm devised to date. Through a series of counterexamples, we show that it is unlikely that a more efficient optimal Pfair algorithm exists. We also introduce the concept of ERfair scheduling, which is a work-conserving extension of Pfair scheduling. In addition, we study the non-optimal earliest-pseudo-deadline-first (EPDF) Pfair algorithm, which is more efficient than PD, and present several scenarios under which it is preferable to PD. We address the flexibility issue by developing the intra-sporadic (IS) task model and by considering the scheduling of dynamic task systems. The well-known sporadic model generalizes the periodic model by allowing jobs to be released late. The IS model generalizes this notion further by allowing late as well as early subtask releases. Such a generalization is useful for modeling applications in which the instantaneous rate of releases differs greatly from the average rate of releases (e.g., an application that