LINTS^RT: A Learning-driven Testbed for Intelligent Scheduling in Embedded Systems

TL;DR

LINTS^RT is a novel learning-driven testbed for real-time scheduling in embedded systems, capable of handling complexities like non-preemption and resource heterogeneity, and outperforming traditional schedulers in schedulability.

Contribution

This work introduces the first extensible learning-based testbed for autonomous real-time scheduling, inspired by AlphaGo Zero, addressing practical complexities in embedded systems.

Findings

LINTS^RT achieves higher schedulability than traditional schedulers.

The framework effectively handles non-preemption and resource heterogeneity.

Implementation demonstrates practical viability and extensibility.

Abstract

Due to the increasing complexity seen in both workloads and hardware resources in state-of-the-art embedded systems, developing efficient real-time schedulers and the corresponding schedulability tests becomes rather challenging. Although close to optimal schedulability performance can be achieved for supporting simple system models in practice, adding any small complexity element into the problem context such as non-preemption or resource heterogeneity would cause significant pessimism, which may not be eliminated by any existing scheduling technique. In this paper, we present LINTS^RT, a learning-based testbed for intelligent real-time scheduling, which has the potential to handle various complexities seen in practice. The design of LINTS^RT is fundamentally motivated by AlphaGo Zero for playing the board game Go, and specifically addresses several critical challenges due to the real-time scheduling context. We first present a clean design of LINTS^RT for supporting the basic case: scheduling sporadic workloads on a homogeneous multiprocessor, and then demonstrate how to easily extend the framework to handle further complexities such as non-preemption and resource heterogeneity. Both application and OS-level implementation and evaluation demonstrate that LINTS^RT is able to achieve significantly higher runtime schedulability under different settings compared to perhaps the most commonly applied schedulers, global EDF, and RM. To our knowledge, this work is the first attempt to design and implement an extensible learning-based testbed for autonomously making real-time scheduling decisions.