Power-Aware Runtime Scheduler for Mixed-Criticality Systems on Multicore Platform

• Published in: IEEE transactions on computer-aided design of integrated circuits and systems Vol. 40; no. 10; pp. 2009 - 2023
• Main Authors: Ranjbar, Behnaz, Nguyen, Tuan D. A., Ejlali, Alireza, Kumar, Akash
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-10-2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Dynamic slack; Heuristic; mixed-criticality (MC) systems; multicore platform; Multicore processing; Power consumption; Power demand; Reduction; Runtime; runtime management; Scheduling; Task analysis; Task scheduling; Thermal management; Timing; timing overhead; Vehicle dynamics
• ISSN: 0278-0070; 1937-4151
• DOI: 10.1109/TCAD.2020.3033374

In modern multicore mixed-criticality (MC) systems, a rise in peak power consumption due to parallel execution of tasks with maximum frequency, specially in the overload situation, may lead to thermal issues, which may affect the reliability and timeliness of MC systems. Therefore, managing peak power consumption has become imperative in multicore MC systems. In this regard, we propose an online peak power and thermal management heuristic for multicore MC systems. This heuristic reduces the peak power consumption of the system as much as possible during runtime by exploiting dynamic slack and per-cluster dynamic voltage and frequency scaling (DVFS). Specifically, our approach examines multiple tasks ahead to determine the most appropriate one for slack assignment, that has the most impact on the system peak power and temperature. However, changing the frequency and selecting a proper task for slack assignment and a proper core for task remapping at runtime can be time-consuming and may cause deadline violation which is not admissible for high-criticality tasks. Therefore, we analyze and then optimize our runtime scheduler and evaluate it for various platforms. The proposed approach is experimentally validated on the ODROID-XU3 (DVFS-enabled heterogeneous multicore platform) with various embedded real-time benchmarks. Results show that our heuristic achieves up to 5.25% reduction in system peak power and 20.33% reduction in maximum temperature compared to an existing method while meeting deadline constraints in different criticality modes.