ML for RT: Priority Assignment Using Machine Learning

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.