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.

翻译：云计算领域的快速发展导致数据中心的碳排放量急剧增加，目前已占全球温室气体排放总量的3%以上，亟需采取紧急措施应对其对全球气候造成的日益严峻压力。这项工作的重点之一是通过提高资源利用率来节约电力消耗。我们提出的全规模自动化（FSA）机制是一种有效方法，能够动态调整资源以适应大规模云计算集群中不断变化的工作负载，使数据中心集群保持目标CPU利用率，从而提升能源效率。与以往依赖统计模型和专家知识调整计算资源的自动缩放方法（如Autopilot或FIRM）不同，FSA利用深度表示学习的强大能力准确预测每个服务的未来工作负载，并自动稳定相应的目标CPU使用水平。在真实数据集上，我们的方法相比现有工作实现了显著的性能提升。我们还将FSA部署在工业数据中心的大规模云计算集群中，根据中国环境联合认证中心（CEC）的认证，在2022年双十一购物节期间实现了947吨二氧化碳减排，相当于节省153.8万千瓦时电力，这标志着我们公司迈向2030年碳中和战略目标的关键一步。