This paper addresses the critical challenge of managing Quality of Service
(QoS) in cloud services, focusing on the nuances of individual tenant
expectations and varying Service Level Indicators (SLIs). It introduces a novel
approach utilizing Deep Reinforcement Learning for tenant-specific QoS
management in multi-tenant, multi-accelerator cloud environments. The chosen
SLI, deadline hit rate, allows clients to tailor QoS for each service request.
A novel online scheduling algorithm for Deep Neural Networks in
multi-accelerator systems is proposed, with a focus on guaranteeing
tenant-wise, model-specific QoS levels while considering real-time constraints.

这篇论文解决了云服务中管理服务质量（QoS）的关键挑战，着重研究了个别租户期望和不同服务水平指标（SLI）的细微差别。它引入了一种新的方法，利用深度强化学习在多租户、多加速器的云环境中进行租户特定的 QoS 管理。所选择的 SLI，即截止时间满足率，允许客户为每个服务请求量身定制服务质量。提出了一种新颖的在线调度算法，针对多加速器系统中的深度神经网络，着重保证租户层面和模型特定的 QoS 水平，并考虑实时约束。