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%.

翻译：多工作负载间的资源共享已成为云服务提供商的普遍实践，其动机在于提升资源利用率并降低总体拥有成本。然而，由于资源争用可能对具有严格服务质量（QoS）要求的高优先级、面向用户的工作负载产生不利影响，有效的资源共享仍是一个尚未解决的开放性挑战。尽管近期方法已展现出有希望的结果，但这些工作在公有云环境中仍缺乏实用性，原因是工作负载无法预先得知且运行时间可能极短，从而阻碍了离线学习并显著制约在线学习。本文提出RAPID，一种适用于高度动态运行环境的快速全在线资源分配策略学习新型框架。RAPID利用轻量级QoS预测（通过基于领域知识的样本效率提升与偏差降低技术实现），将控制机制与传统反馈源解耦，并以比先前工作快数个数量级的速度指导策略学习。在搭载代表性云工作负载的真实服务器平台上的评估证实，RAPID可在数分钟内（而先前最先进方法需要数小时）学习到稳定的资源分配策略，同时将QoS提升9.0倍，并将尽力而为工作负载性能提高19-43%。