Optimal Priority Assignment for Real-Time Systems: A Coevolution-Based Approach

TL;DR

This paper introduces a coevolutionary algorithm to automatically assign optimal priorities to real-time tasks, improving safety margins and deadline adherence in complex industrial systems.

Contribution

It presents a novel multi-objective coevolutionary approach that automates priority assignment, outperforming traditional methods and scalable for industrial applications.

Findings

Outperforms random and sequential search methods.

Effective on six industrial and synthetic systems.

Scales within 16 hours for complex systems.

Abstract

In real-time systems, priorities assigned to real-time tasks determine the order of task executions, by relying on an underlying task scheduling policy. Assigning optimal priority values to tasks is critical to allow the tasks to complete their executions while maximizing safety margins from their specified deadlines. This enables real-time systems to tolerate unexpected overheads in task executions and still meet their deadlines. In practice, priority assignments result from an interactive process between the development and testing teams. In this article, we propose an automated method that aims to identify the best possible priority assignments in real-time systems, accounting for multiple objectives regarding safety margins and engineering constraints. Our approach is based on a multi-objective, competitive coevolutionary algorithm mimicking the interactive priority assignment process between the development and testing teams. We evaluate our approach by applying it to six industrial systems from different domains and several synthetic systems. The results indicate that our approach significantly outperforms both our baselines, i.e., random search and sequential search, and solutions defined by practitioners.Our approach scales to complex industrial systems as an offline analysis method that attempts to find near-optimal solutions within acceptable time, i.e., less than 16 hours.