Scalable Multi-Tasking using Preemption Thresholds

Preemptive multi-tasking is a commonly used architecture for designing and implementing embedded real-time software. However, preemptive multi-tasking comes with its own costs. These costs include overheads due to preemptions and context-switches that result in waste of CPU bandwidth. Also, each task incurs a memory cost largely due to the need to maintain a separate stack for each task. These costs increase with the number of tasks and can be significant in complex real-time software. In this paper, we propose results from our ongoing research in which we are developing a design method with scalable multi-tasking implementations for complex realtime software. Our design method is based on an extension of fixed priority preemptive scheduling using preemption thresholds that was proposed in [15]. Using this new scheduling model we show how we can design multi-tasking implementations that are far more scalable than using pure preemptive multi-tasking implementations.