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

Distributed, Parallel, and Cluster Computing