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AG Audience

English

60 Minutes

Kaiserslautern

From video and music to responsive UIs, a lot of real-time workloads run on today's desktops and mobile devices, but real-time scheduling interfaces in commodity operating systems have not gained traction. As a result, the CPU scheduler receives no explicit knowledge about applications' needs and thus falls back to heuristics or best-effort operation.
I present ATLAS - the Auto-Training Look-Ahead Scheduler. ATLAS improves service to applications with regard to two non-functional properties: timeliness and overload detection. ATLAS provides timely service to applications, accessible through an easy-to-use interface. Deadlines specify timing requirements, workload metrics describe jobs. ATLAS employs machine learning to predict job execution times. Deadline misses are detected before they occur, so applications can react early.
ATLAS is currently a single-core scheduler, so after presenting the status quo I will discuss the planned multicore extension and the new application scenarios it enables.

Vera Laubscher

0631-93039600

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Yes

Saarbrücken

E1 5

029

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