Work-in-Progress: LEETMIC: Reinforcement Learning-Based Energy-Efficient Task Scheduling in Multicore Cyber-Physical Systems

Erfan Bagheri Soula, Moein Esnaashari, Sepideh Safari, Mohsen Ansari

Abstract: Energy efficiency is a critical design constraint in multicore Cyber-Physical Systems (CPS). Using energy management methods can violate timing constraints; hence, designing scheduling policies that can adapt to the dynamic and unpredictable nature of aperiodic real-time tasks remains a significant challenge. This paper introduces LEETMIC, a novel deep reinforcement learning framework for energy-aware real-time scheduling. To manage a variable number of active jobs, LEETMIC utilizes a learned policy network to determine the scheduling priority and Dynamic Voltage and Frequency Scaling (DVFS) level for each task individually, based on a combination of the task's local attributes and a summary of the global system state. The policy is trained using Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm to co-optimize for both task schedulability and energy consumption. The trained policy neural network is a compact multi-layer perceptron (MLP), making it suitable for online deployment in embedded systems. Experimental results demonstrate that, compared to the Global Earliest Deadline First (GEDF) scheduler, LEETMIC achieves a similar success ratio while significantly reducing energy consumption by 41.5% on average (up to 70%).

External IDs:dblp:conf/rtss/SoulaESA25