Managing Varying Worst Case Execution Times on DVS Platforms

TL;DR

This paper explores methods for managing variable worst case execution times in energy-efficient real-time scheduling on DVS platforms, demonstrating that low deadline misses and energy use are achievable even with uncertain execution bounds.

Contribution

It introduces approaches to handle fluctuating worst case execution times, relaxing the assumption of precise knowledge, and shows these methods perform well in simulations.

Findings

Low deadline miss rates achieved

Energy consumption comparable to clairvoyant algorithms

Effective with minimal additional effort

Abstract

Energy efficient real-time task scheduling attracted a lot of attention in the past decade. Most of the time, deterministic execution lengths for tasks were considered, but this model fits less and less with the reality, especially with the increasing number of multimedia applications. It's why a lot of research is starting to consider stochastic models, where execution times are only known stochastically. However, authors consider that they have a pretty much precise knowledge about the properties of the system, especially regarding to the worst case execution time (or worst case execution cycles, WCEC). In this work, we try to relax this hypothesis, and assume that the WCEC can vary. We propose miscellaneous methods to react to such a situation, and give many simulation results attesting that with a small effort, we can provide very good results, allowing to keep a low deadline miss rate as well as an energy consumption similar to clairvoyant algorithms.