Work-in-Progress: Non-preemptive Scheduling of Periodic Tasks with Data Dependency Upon Heterogeneous Multiprocessor Platforms

Abstract

Heterogeneous multiprocessor platforms have been widely adopted as an efficient approach to providing high instruction throughput while keeping power and complexity under control. Although this approach can achieve improved performance for large-scale real-time systems, it results in a complex task scheduling problem. All tasks should be scheduled according to a proper strategy such that their deadlines will be met even in the worst case situations. In this work, we study the non-preemptive scheduling problem of periodic tasks with data dependency upon heterogeneous multiprocessor platforms. We first analyze the space, time and precedence constraints of tasks, and propose an exact formulation to determine the schedulability of tasks. Then, inspired from the Heterogeneous Earliest Finish Time (HEFT) algorithm, we present a list-based scheduling heuristic to schedule the jobs generated by the periodic tasks and minimize the jobs' finish time. The proposed approach is efficient and can help in guiding the design of heterogeneous multiprocessor systems.