{"type":"ScholarlyArticle","title":"Full Scaling Automation for Sustainable Development of Green Data Centers","authors":[{"name":"Shiyu Wang","slug":"shiyu-wang","given":"Shiyu","family":"Wang","affiliation":""},{"name":"Yinbo Sun","slug":"yinbo-sun","given":"Yinbo","family":"Sun","affiliation":""},{"name":"Xiaoming Shi","slug":"xiaoming-shi","given":"Xiaoming","family":"Shi","affiliation":""},{"name":"Zhu Shiyi","slug":"zhu-shiyi","given":"Zhu","family":"Shiyi","affiliation":""},{"name":"Lin-Tao Ma","slug":"lin-tao-ma","given":"Lin-Tao","family":"Ma","affiliation":""},{"name":"James Zhang","slug":"james-zhang","given":"James","family":"Zhang","affiliation":""},{"name":"YangFei Zheng","slug":"yangfei-zheng","given":"YangFei","family":"Zheng","affiliation":""},{"name":"Liu Jian","slug":"liu-jian","given":"Liu","family":"Jian","affiliation":""}],"abstract":"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.","doi":"10.24963/ijcai.2023/695","url":"https://ijcai.org/proceedings/2023/695","pdf":"https://ijcai.org/proceedings/2023/0695.pdf","markdown":"https://ijcai.org/proceedings/2023/695.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/695","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"AI for Good","track":"AI for Good","number":695,"pages":{"first":6264,"last":6271},"keywords":["AI for Good: Machine Learning","AI for Good: Data Mining"],"videos":[],"references":[{"position":1,"raw":"[Abdullah et al., 2020] Muhammad Abdullah, Waheed Iqbal, Josep Lluis Berral, Jorda Polo, and David Carrera. 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