Summary
Asia-Pacific Network Operations and Management Symposium
2022
Session Number:TS5
Session:
Number:TS5-05
DScaler: A Horizontal Autoscaler of Microservice Based on Deep Reinforcement Learning
Zhijiao Xiao, Song Hu,
pp.-
Publication Date:2022/09/28
Online ISSN:2188-5079
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Summary:
With the development of container technology, mi croservice 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.