Energy-efficient scheduling of imprecise mixed-criticality real-time tasks based on genetic algorithm

Abstract

Previous work focuses on the problem of energy for Imprecise Mixed-Criticality Systems on multiprocessor platforms, which provides degraded services for low criticality tasks in the high criticality mode. However, the proposed solution based on the partitioning heuristic Criticality-Unaware Worst Fit Decreasing method (CU-WFD) may consume more energy. In this paper, we propose a novel partitioning algorithm named EEAA based on the genetic algorithm and explore various parameter combinations, aiming to find a task-to-processor assignment method that can further reduce energy consumption with the best parameter combinations. In addition, we conduct experiments to evaluate EEAA and the experimental result shows that EEAA achieves energy savings of 12.56% compared to CU-WFD.