Assigning Optimal Integer Harmonic Periods to Hard Real Time Tasks

TL;DR

This paper introduces an optimal algorithm for assigning integer harmonic periods to real-time tasks within application constraints, improving efficiency over brute-force methods and ensuring system schedulability.

Contribution

The paper presents the Discrete Piecewise Harmonic Search (DPHS) algorithm for optimal harmonic period assignment, addressing the discrete nature of real-time system timing and proving its optimality under rational metrics.

Findings

DPHS finds optimal task assignments with up to 94% fewer searches than brute-force methods.

The algorithm guarantees optimal solutions for rational metrics like TPE, TSU, FOE, and MPE.

Application to real-world task sets demonstrates practical benefits and efficiency.

Abstract

Selecting period values for tasks is a very important step in the design process of a real-time system, especially due to the significance of its impact on system schedulability. It is well known that, under RMS, the utilization bound for a harmonic task set is 100%. Also, polynomial-time algorithms have been developed for response-time analysis of harmonic task sets. In practice, the largest acceptable value for the period of a task is determined by the performance and safety requirements of the application. In this paper, we address the problem of assigning harmonic periods to a task set such that every task gets assigned an integer period less than or equal to its application specified upper bound and the task utilization of every task is less than 1. We focus on integer solutions given the discrete nature of time in real-time computer systems. We first express this problem of assigning harmonic periods to a task set as a discrete piecewise optimization problem. We then present the 'Discrete Piecewise Harmonic Search' (DPHS) algorithm that outputs an optimal harmonic task assignment. We then define conditions for a metric to be rational for harmonization. We show that commonly used metrics like, the total percentage error (TPE), total system utilization (TSU), first order error (FOE), and maximum percentage error (MPE), are rational. We next prove that the DPHS algorithm finds the optimal feasible assignment, if one exists, for these rational metrics. We apply the DPHS algorithm to harmonize task sets used in real-world applications to highlight its benefits. We compare the performance of the DPHS algorithm against a brute-force search and find that the DPHS searches up to 94\% fewer task sets than the brute-force search that obtains the optimal solution.