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Cloud computing has attracted both end-users and Cloud Service Providers(CSPs) in recent years. Improving resource utilization rate (RUtR), such as CPUand memory usages on servers, while maintaining Quality-of-Service (QoS) is onekey challenge faced by CSPs with warehouse-scale data centers. Prior worksproposed various algorithms to reduce energy cost or to improve RUtR, whicheither lack the fine-grained task scheduling capabilities, or fail to take acomprehensive system model into consideration. This article presents H2O-Cloud,a Hierarchical and Hybrid Online task scheduling framework for warehouse-scaleCSPs, to improve resource usage effectiveness while maintaining QoS. H2O-Cloudis highly scalable and considers comprehensive information such as variousworkload scenarios, cloud platform configurations, user request information anddynamic pricing model. The hierarchy and hybridity of the framework, combinedwith its deep reinforcement learning (DRL) engines, enable H2O-Cloud toefficiently start on-the-go scheduling and learning in an unpredictableenvironment without pre-training. Our experiments confirm the high efficiencyof the proposed H2O-Cloud when compared to baseline approaches, in terms ofenergy and cost while maintaining QoS. Compared with a state-of-the-artDRL-based algorithm, H2O-Cloud achieves up to 201.17% energy cost efficiencyimprovement, 47.88% energy efficiency improvement and 551.76% reward rateimprovement.

H2O-Cloud: A Resource and Quality of Service-Aware Task Scheduling  Framework for Warehouse-Scale Data Centers -- A Hierarchical Hybrid DRL (Deep  Reinforcement Learning) based Approach