AHPA: Adaptive Horizontal Pod Autoscaling Systems on Alibaba Cloud Container Service for Kubernetes


  • Zhiqiang Zhou DAMO Academy, Alibaba Group
  • Chaoli Zhang DAMO Academy, Alibaba Group
  • Lingna Ma DAMO Academy, Alibaba Group
  • Jing Gu Alibaba Cloud Group
  • Huajie Qian DAMO Academy, Alibaba Group
  • Qingsong Wen DAMO Academy, Alibaba Group
  • Liang Sun DAMO Academy, Alibaba Group
  • Peng Li Alibaba Cloud Group
  • Zhimin Tang Alibaba Cloud Group




Autoscaling, Time Series Forecasting, Operations Research, AIOps


The existing resource allocation policy for application instances in Kubernetes cannot dynamically adjust according to the requirement of business, which would cause an enormous waste of resources during fluctuations. Moreover, the emergence of new cloud services puts higher resource management requirements. This paper discusses horizontal POD resources management in Alibaba Cloud Container Services with a newly deployed AI algorithm framework named AHPA - the adaptive horizontal pod auto-scaling system. Based on a robust decomposition forecasting algorithm and performance training model, AHPA offers an optimal pod number adjustment plan that could reduce POD resources and maintain business stability. Since being deployed in April 2021, this system has expanded to multiple customer scenarios, including logistics, social networks, AI audio and video, e-commerce, etc. Compared with the previous algorithms, AHPA solves the elastic lag problem, increasing CPU usage by 10% and reducing resource cost by more than 20%. In addition, AHPA can automatically perform flexible planning according to the predicted business volume without manual intervention, significantly saving operation and maintenance costs.




How to Cite

Zhou, Z., Zhang, C., Ma, L., Gu, J., Qian, H., Wen, Q., Sun, L., Li, P., & Tang, Z. (2023). AHPA: Adaptive Horizontal Pod Autoscaling Systems on Alibaba Cloud Container Service for Kubernetes. Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), 15621-15629. https://doi.org/10.1609/aaai.v37i13.26852



IAAI Technical Track on deployed Highly Innovative Applications of AI