Joint Time-and Event-Triggered Scheduling in the Linux Kernel

TL;DR

This paper introduces a novel low-overhead time-triggered scheduling class for the Linux kernel, enabling deterministic, table-driven scheduling of real-time tasks on multicore systems, enhancing Linux's real-time capabilities.

Contribution

It presents a new time-triggered scheduling class and a slot shifting manager for Linux, improving real-time predictability and task isolation without high overhead.

Findings

Supports offline table-driven scheduling of real-time tasks.

Provides guaranteed execution time for aperiodic tasks.

Evaluated successfully on Intel Xeon hardware.

Abstract

There is increasing interest in using Linux in the real-time domain due to the emergence of cloud and edge computing, the need to decrease costs, and the growing number of complex functional and non-functional requirements of real-time applications. Linux presents a valuable opportunity as it has rich hardware support, an open-source development model, a well-established programming environment, and avoids vendor lock-in. Although Linux was initially developed as a general-purpose operating system, some real-time capabilities have been added to the kernel over many years to increase its predictability and reduce its scheduling latency. Unfortunately, Linux currently has no support for time-triggered (TT) scheduling, which is widely used in the safety-critical domain for its determinism, low run-time scheduling latency, and strong isolation properties. We present an enhancement of the Linux scheduler as a new low-overhead TT scheduling class to support offline table-driven scheduling of tasks on multicore Linux nodes. Inspired by the Slot shifting algorithm, we complement the new scheduling class with a low overhead slot shifting manager running on a non-time-triggered core to provide guaranteed execution time to real-time aperiodic tasks by using the slack of the time-triggered tasks and avoiding high-overhead table regeneration for adding new periodic tasks. Furthermore, we evaluate our implementation on server-grade hardware with Intel Xeon Scalable Processor.