DScaler: A Horizontal Autoscaler of Microservice Based on Deep Reinforcement Learning

With the development of container technology, microservice architecture has become a powerful paradigm for cloud computing with efficient infrastructure management and large-scale service capabilities. Cloud providers require flexible resource management to meet dynamic workloads, such as autoscaling and provisioning. As one of the most popular open-source container orchestration systems, Kubernetes provides a built-in mechanism, Horizontal Pod Autoscaler (HPA), for dynamic resource autoscaling. However, the static rules of HPA are not adaptable to highly dynamic workloads. In this paper, we propose a deep reinforcement learning-based horizontal autoscaler(DScaler) for autoscaling of microservices deployed in Kubernetes. Under two workloads with different characteristics, our experiments show that the proposed approach reduces resource consumption by 19.90% and 10.80% while reducing SLA violations by 8.56% and 12.75% compared with HPA, respectively. In addition, our approach can significantly reduce resource consumption by about 60% compared to the existing reinforcement learning strategy while maintaining SLA within an acceptable level.