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Resource sharing between multiple workloads has become a prominent practiceamong cloud service providers, motivated by demand for improved resourceutilization and reduced cost of ownership. Effective resource sharing, however,remains an open challenge due to the adverse effects that resource contentioncan have on high-priority, user-facing workloads with strict Quality of Service(QoS) requirements. Although recent approaches have demonstrated promisingresults, those works remain largely impractical in public cloud environmentssince 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-onlineresource allocation policy learning in highly dynamic operating environments.RAPID leverages lightweight QoS predictions, enabled bydomain-knowledge-inspired techniques for sample efficiency and bias reduction,to decouple control from conventional feedback sources and guide policylearning at a rate orders of magnitude faster than prior work. Evaluation on areal-world server platform with representative cloud workloads confirms thatRAPID can learn stable resource allocation policies in minutes, as comparedwith hours in prior state-of-the-art, while improving QoS by 9.0x andincreasing best-effort workload performance by 19-43%.

RAPID: Enabling Fast Online Policy Learning in Dynamic Public Cloud Environments