A Framework for Automated Competitive Analysis of On-line Scheduling of Firm-Deadline Tasks

TL;DR

This paper introduces a flexible framework for automatically analyzing and comparing the performance of online scheduling algorithms for real-time tasks with firm deadlines, using multi-objective graphs to compute competitive ratios.

Contribution

The paper presents a novel automated framework that evaluates and compares online scheduling algorithms' competitiveness for real-time tasks, accommodating various constraints and tasksets.

Findings

No single algorithm is universally optimal across all tasksets.

The framework effectively compares multiple algorithms' competitive ratios.

Experimental results guide optimal algorithm selection for specific applications.

Abstract

We present a flexible framework for the automated competitive analysis of on-line scheduling algorithms for firm-deadline real-time tasks based on multi-objective graphs: Given a taskset and an on-line scheduling algorithm specified as a labeled transition system, along with some optional safety, liveness, and/or limit-average constraints for the adversary, we automatically compute the competitive ratio of the algorithm w.r.t. a clairvoyant scheduler. We demonstrate the flexibility and power of our approach by comparing the competitive ratio of several on-line algorithms, including $D^{over}$, that have been proposed in the past, for various tasksets. Our experimental results reveal that none of these algorithms is universally optimal, in the sense that there are tasksets where other schedulers provide better performance. Our framework is hence a very useful design tool for selecting optimal algorithms for a given application.