GLEAM: A Graph-Learning Enhanced Adaptive Metaheuristic for Power-Aware Scheduling on Heterogeneous Cyber-Physical Systems

The increasing complexity of embedded and Cyber-Physical Systems (CPS) has accelerated the adoption of heterogeneous multi-core architectures, which combine performance and energy efficiency. However, scheduling dependent tasks on such platforms introduces significant challenges due to strict real-time constraints, high energy consumption, and the NP-hard nature of task mapping. This paper proposes a novel hybrid scheduling framework to jointly optimize energy efficiency and timeliness for Directed Acyclic Graph (DAG) applications. The framework operates in three tiers: first, a Genetic Algorithm (GA) performs a global search to determine near-optimal task-to-core mappings; second, a Dynamic Voltage and Frequency Scaling (DVFS) manager is integrated into the GA’s fitness function to accurately capture energy-performance trade-offs; and third, a Graph Neural Network (GNN) is trained to imitate the GA+DVFS policy, enabling fast and high-quality online scheduling decisions. Experimental results demonstrate that the proposed approach achieves a balanced trade-off between power consumption and deadline satisfaction, while the GNN significantly accelerates scheduling without compromising solution quality. ... mehrOur GLEAM method reduced energy consumption on average by 49.08% and improved the makespan on average by 27.03% compared to baseline methods.