Resource sharing between multiple workloads has become a prominent practice
among cloud service providers, motivated by demand for improved resource
utilization and reduced cost of ownership. Effective resource sharing, however,
remains an open challenge due to the adverse effects that resource contention
can have on high-priority, user-facing workloads with strict Quality of Service
(QoS) requirements. Although recent approaches have demonstrated promising
results, those works remain largely impractical in public cloud environments
since workloads are not known in advance and may only run for a brief period,
thus prohibiting offline learning and significantly hindering online learning.
In this paper, we propose RAPID, a novel framework for fast, fully-online
resource allocation policy learning in highly dynamic operating environments.
RAPID leverages lightweight QoS predictions, enabled by
domain-knowledge-inspired techniques for sample efficiency and bias reduction,
to decouple control from conventional feedback sources and guide policy
learning at a rate orders of magnitude faster than prior work. Evaluation on a
real-world server platform with representative cloud workloads confirms that
RAPID can learn stable resource allocation policies in minutes, as compared
with hours in prior state-of-the-art, while improving QoS by 9.0x and
increasing best-effort workload performance by 19-43%.

提出了一种 RAPID 框架，它能够在高度动态的操作环境中实现快速的、完全在线的资源分配策略学习，从而有效地共享资源，提高服务利用率和降低所有权成本。RAPID 利用轻量级的 QoS 预测，通过启用领域知识启发技术来实现样本效率和偏差降低，以比先前的工作快几个数量级的速率来指导策略学习，从而实现学习稳定的资源分配策略，提高 QoS 9 倍，并提高了 19-43% 的最佳努力工作性能。