  The rapid rise in cloud computing has resulted in an alarming increase in
data centers' carbon emissions, which now accounts for >3% of global greenhouse
gas emissions, necessitating immediate steps to combat their mounting strain on
the global climate. An important focus of this effort is to improve resource
utilization in order to save electricity usage. Our proposed Full Scaling
Automation (FSA) mechanism is an effective method of dynamically adapting
resources to accommodate changing workloads in large-scale cloud computing
clusters, enabling the clusters in data centers to maintain their desired CPU
utilization target and thus improve energy efficiency. FSA harnesses the power
of deep representation learning to accurately predict the future workload of
each service and automatically stabilize the corresponding target CPU usage
level, unlike the previous autoscaling methods, such as Autopilot or FIRM, that
need to adjust computing resources with statistical models and expert
knowledge. Our approach achieves significant performance improvement compared
to the existing work in real-world datasets. We also deployed FSA on
large-scale cloud computing clusters in industrial data centers, and according
to the certification of the China Environmental United Certification Center
(CEC), a reduction of 947 tons of carbon dioxide, equivalent to a saving of
1538,000 kWh of electricity, was achieved during the Double 11 shopping
festival of 2022, marking a critical step for our company's strategic goal
towards carbon neutrality by 2030.
