SoCRATES: System-on-Chip Resource Adaptive Scheduling using Deep Reinforcement Learning

Deep Reinforcement Learning (DRL) widely applies to job scheduling and resource allocation applications and remarkably improves performances. Existing resource management approaches essentially optimize makespan minimization on the fixed number of jobs. Although previous researches purport to express breakthrough performances, we alternatively corroborate the job scheduling problem in a more practical scenario where the scheduler (1) aims to minimize average latency in a steady-state condition and (2) assigns varying numbers of tasks to heterogeneous resources. Broadly, these conditions are essential in the System-on-Chip (SoC) application, which developed with high-frequency job generation and operated distributed resources in a parallel. We assume the indefinite jobs are continuously injected into the simulation in a stream fashion and empirically discover that existing research hard adapts in the suggested scenario. Moreover, the agent must tackle concurrent observations caused by the task dependency. This paper introduces the Eclectic Interaction Matching technique to tackle such difficulties and the System-on-Chip Resource AdapTivE Scheduling (SoCRATES) that specialized in scheduling hierarchical jobs to heterogeneous resources the SoC application. The proposed method outperforms other existing RL-based schedulers and state-of-the-art heuristic schedulers.