MARS：可延展的演员-评论家强化学习模型
MARS: Malleable Actor-Critic Reinforcement Learning Scheduler
摘要：
在本文中，我们介绍了MARS，一个新的调度系统的HPC云基础设施的基础上，成本敏感，灵活的强化学习方法，作为一个中间层，为下一代HPC云资源管理器。MARS集成来自启发式工作负载的预训练模型，并决定最具成本效益的优化策略。将整个工作流应用分解为若干个可优化的相关子任务，然后根据预定义的资源管理计划，在执行调度任务后产生奖励。最后，MARS基于奖励更新深度神经网络（DNN）模型。MARS旨在通过强化机制优化现有模型。MARS适应工作流应用的动态性，在预先构建的调度策略（如：JFS、SJF等）中选择最具成本效益的调度方案。以及在运行时自学习深度神经网络模型。我们评估MARS与不同的现实世界的工作流程的痕迹。与最先进的方法相比，MARS可以实现5%-60%的性能提高。
英文摘要：
In this paper, we introduce MARS, a new scheduling system for HPC-cloud infrastructures based on a cost-aware, flexible reinforcement learning approach, which serves as an intermediate layer for next generation HPC-cloud resource manager. MARS ensembles the pre-trained models from heuristic workloads and decides on the most cost-effective strategy for optimization. A whole workflow application would be split into several optimizable dependent sub-tasks, then based on the pre-defined resource management plan, a reward will be generated after executing a scheduled task. Lastly, MARS updates the Deep Neural Network (DNN) model based on the reward. MARS is designed to optimize the existing models through reinforcement mechanisms. MARS adapts to the dynamics of workflow applications, selects the most cost-effective scheduling solution among pre-built scheduling strategies (backfilling, SJF, etc.) and self-learning deep neural network model at run-time. We evaluate MARS with different real-world workflow traces. MARS can achieve 5%-60% increased performance compared to the state-of-the-art approaches.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
