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.

中文翻译：

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SoCRATES：使用深度强化学习的片上系统资源自适应调度

深度强化学习（DRL）广泛应用于作业调度和资源分配应用程序，并显着提高了性能。现有的资源管理方法实质上是在固定数量的作业上优化了制造周期的最小化。尽管先前的研究旨在表达突破性的性能，但我们还是在更实际的情况下证实了作业调度问题，其中调度器（1）旨在最大程度地减少稳态条件下的平均延迟，并且（2）将不同数量的任务分配给异构资源。广义上讲，这些条件对于片上系统（SoC）应用程序是必不可少的，该应用程序是通过高频作业生成和并行操作分布式资源而开发的。我们假设不确定的工作以流的方式不断地注入到仿真中，并凭经验发现现有的研究很难适应建议的方案。而且，代理必须处理由任务依赖性引起的并发观察。本文介绍了折衷交互匹配技术来解决此类难题，并介绍了片上系统资源自适应调度（SoCRATES），专门用于将分层作业调度到SoC应用程序的异构资源。所提出的方法优于其他现有的基于RL的调度程序和最新的启发式调度程序。本文介绍了折衷交互匹配技术来解决此类难题，并介绍了片上系统资源自适应调度（SoCRATES），专门用于将分层作业调度到SoC应用程序的异构资源。所提出的方法优于其他现有的基于RL的调度程序和最新的启发式调度程序。本文介绍了折衷交互匹配技术来解决此类难题，并介绍了片上系统资源自适应调度（SoCRATES），专门用于将分层作业调度到SoC应用程序的异构资源。所提出的方法优于其他现有的基于RL的调度程序和最新的启发式调度程序。