DeepScaling: Autoscaling Microservices With Stable CPU Utilization for Large Scale Production Cloud Systems
Ziliang Wang, Shiyi Zhu, Jianguo Li, Wei Jiang, K. K. Ramakrishnan, Meng Yan, Xiaohong Zhang, Alex X. Liu
Abstract
Cloud service providers often provision excessive resources to meet the desired Service Level Objectives (SLOs), by setting lower CPU utilization targets. This can result in a waste of resources and a noticeable increase in power consumption in large-scale cloud deployments. To address this issue, this paper presents DeepScaling, an innovative solution for minimizing resource cost while ensuring SLO requirements are met in a dynamic, large-scale production microservice-based system. We propose DeepScaling, which introduces three innovative components to adaptively refine the target CPU utilization of servers in the data center, and we maintain it at a stable value to meet SLO constraints while using minimum amount of system resources. First, DeepScaling forecasts workloads for each service using a Spatio-temporal Graph Neural Network. Secondly, it estimates CPU utilization with a Deep Neural Network, considering factors such as periodic tasks and traffic. Finally, it uses a modified Deep Q-Network (DQN) to generate an autoscaling policy that controls service resources to maximize service stability while meeting SLOs. Evaluation of DeepScaling in Ant Group’s large-scale cloud environment shows that it outperforms state-of-the-art autoscaling approaches in terms of maintaining stable performance and resource savings. The deployment of DeepScaling in the real-world environment of 1900+ microservices saves the provisioning of over 100,000 CPU cores per day, on average.