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

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