Energy-Aware Adaptive Mixed-Criticality Scheduling with Semi-Clairvoyance and Graceful Degradation

Abstract

The classic Mixed-Criticality System (MCS) task model is a non-clairvoyance model in which the change of the system behavior is based on the completion of high-criticality tasks while dropping low-criticality tasks in high-criticality mode. In this paper, we simultaneously consider graceful degradation and semi-clairvoyance in MCS. We first propose the analysis for adaptive mixed-criticality with semi-clairvoyance denoted as C-AMC-sem. The so-called semi-clairvoyance refers to the system’s behavior change being revealed at the time that jobs are released. Moreover, we propose a new algorithm based on C-AMC-sem to reduce energy consumption. Finally, we verify the performance of the proposed algorithms via experiments upon synthetically generated tasksets. The experimental results indicate that the proposed algorithms significantly outperform the existing algorithms.