CQGA-HEFT: Q-learning-based DAG Scheduling Algorithm Using Genetic Algorithm in Clustered Many-core Platform

Embedded systems, e.g., self-driving are becoming larger and more complex, the performance requirements of such platforms increasing. Clustered many-core processors satisfy these because they provide an isolated computing environment, low power consumption, high performance. However, efficiently scheduling many tasks to clustered is a difficult problem. Therefore, this paper proposes algorithm for based on Q-learning (a reinforcement learning method) that effective tasks. The proposed distinguishes difference in communication time between bus network-on-chip processor. algorithm's process, two types times considered genetic (GA). Comparative experiments demonstrate outperforms existing methods terms makespan.