ML for RT: Priority assignment using machine learning

Seunghoon Lee, Hyeongboo Baek, Honguk Woo, Kang G. Shin, Jinkyu Lee

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations

Abstract

As machine learning (ML) has been proven effective in solving various problems, researchers in the real-time systems (RT) community have recently paid increasing attention to ML. While most of them focused on timing issues for ML applications (i.e., RT for ML), only a little has been done on the use of ML for solving fundamental RT problems. In this paper, we aim at utilizing ML to solve a fundamental RT problem of priority assignment for global fixed-priority preemptive (gFP) scheduling on a multiprocessor platform. This problem is known to be challenging in the case of a large number (n) of tasks in a task set because exhaustive testing of all priority assignments (as many as n!) is intractable and existing heuristics cannot find a schedulable priority assignment, even if exists, for a number of task sets. We systematically incorporate RT domain knowledge into ML and develop an ML framework tailored to the problem, called PAL. First, raising and addressing technical issues including neural architecture selection and training sample regulation, we enable PAL to infer a schedulable priority assignment of a set of n tasks, by training PAL with same-size (i.e., with n tasks) samples each of whose schedulable priority assignment has already been identified. Second, considering the exhaustive testing of all priority assignments of each task set with large n makes it intractable to provide training samples to PAL, we derive inductive properties that can generate training samples with large n from those with small n, through empirical observation of PAL and mathematical analysis of the target gFP schedulability test. Finally, utilizing the inductive properties and additional techniques, we propose how to systematically implement PAL whose training sample generation process not only yields unbiased samples but also is tractable even for large n. Our experimental results demonstrate PAL covers a number of additional task sets, each of which has never been proven schedulable by any existing approaches for gFP.

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE 27th Real-Time and Embedded Technology and Applications Symposium, RTAS 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages118-130
Number of pages13
ISBN (Electronic)9781665403863
DOIs
StatePublished - May 2021
Event27th IEEE Real-Time and Embedded Technology and Applications Symposium, RTAS 2021 - Virtual, Online
Duration: 18 May 202121 May 2021

Publication series

NameProceedings of the IEEE Real-Time and Embedded Technology and Applications Symposium, RTAS
Volume2021-May
ISSN (Print)1545-3421

Conference

Conference27th IEEE Real-Time and Embedded Technology and Applications Symposium, RTAS 2021
CityVirtual, Online
Period18/05/2121/05/21

Keywords

  • ML for RT
  • Machine Learning
  • Priority Assignment
  • Real Time Systems
  • Schedulability Analysis

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