QoS- And Power-Aware Run-Time Scheduler for Multi-core Mixed-Criticality Systems

In modern multi-core MC! systems, a rise in peak power consumption due to parallel execution of tasks with maximum frequency, especially 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 multi-core MC! systems. In this regard, we propose an online peak power and thermal management heuristic for multi-core MC! systems. This heuristic reduces the peak power consumption of the system as much as possible during run-time by exploiting dynamic slack and per-cluster DVFS!. Specifically, our approach examines multiple tasks ahead to determine the most appropriate one for slack assignment, which 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 re-mapping at run-time can be time-consuming and may cause deadline violation which is not admissible for HC! (HC!) tasks. Therefore, we analyze and then optimize our run-time scheduler and evaluate it for various platforms. The proposed approach is experimentally validated on the ODROID-XU3 with various embedded real-time benchmarks.