Sustainability in Mixed-Criticality Scheduling

Guo, Zhishan; Sruti, Sai; Ward, Bryan C.; Baruah, Sanjoy

doi:10.1109/rtss.2017.00010

Cited by 6 publications

(1 citation statement)

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“…Till recently, this classical scheduling problem is deemed to be practically resolved as a heuristic-based algorithm is shown to be able to achieve a rather high runtime schedulability considering real system overheads [10]. Besides this basic system model (i.e., sporadic workloads preemptively scheduled on homogeneous multiprocessor), extensive efforts have been made to handle increasing complexities seen in the task model, such as non-preemptive execution [2], [3], [4], precedence constraints [11], [12], [13], [14], selfsuspensions [15], [16], and mixed-criticality [17], [18], [19], [20], as well as those seen in the hardware platform, such as heterogeneous multiprocessors [21], [22], computing accelerators [23], [24], [25], and shared resources [26], [27], [28]. Unfortunately, although promising progress has been made for dealing with various complexities, many such complexities yield rather pessimistic results.…”

Section: A Real-time Schedulingmentioning

confidence: 99%

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

Kong,

Yadlapalli,

Bateni

et al. 2020

Preprint

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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 nonpreemption and resource heterogeneity. Both application and OSlevel 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 realtime scheduling decisions.

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