Atlas: Look-ahead scheduling using workload metrics

From video and music to user interface animations, a lot of real-time workloads run on today's desktops and mobile devices, yet commodity operating systems offer scheduling interfaces like nice-levels, priorities or shares that do not adequately convey timing requirements. Real-time research offers many solutions with strong timeliness guarantees, but they often require a periodic task model and ask the developer for information that is hard to obtain like execution times or reservation budgets. Within this design space of easy programming, but weak guarantees on one hand and strong guarantees, but harder development on the other, we propose Atlas, the Auto-Training Look-Ahead Scheduler. With a simple yet powerful interface it relies exclusively on data from the application domain: It uses deadlines to express timing requirements and workload metrics to express resource requirements. It replaces implicit knowledge of future job releases as provided by periodic tasks with explicit job submission to enable look-ahead scheduling. Using video playback as a dynamic high-throughput load, we show that the proposed workload metrics are sufficient for Atlas to know an application's execution time behavior ahead of time. Atlas' predictions have a typical relative error below 10%.