Predictive Container Auto-Scaling for Cloud-Native Applications
- Authors
- Zhao, Hanqing; Lim, Hyunwoo; Hanif, Muhammad; Lee, Choonhwa
- Issue Date
- Oct-2019
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- Auto-Scaling; Cloud-native Application; Container; Microservices
- Citation
- ICTC 2019 - 10th International Conference on ICT Convergence: ICT Convergence Leading the Autonomous Future, pp.1280 - 1282
- Indexed
- SCOPUS
- Journal Title
- ICTC 2019 - 10th International Conference on ICT Convergence: ICT Convergence Leading the Autonomous Future
- Start Page
- 1280
- End Page
- 1282
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/146997
- DOI
- 10.1109/ICTC46691.2019.8939932
- ISSN
- 2162-1233
- Abstract
- In the past decade, cloud computing has become an essential technology in many areas such as Internet of Things, artificial intelligence, and social media. In the cloud-computing environment, the auto-scaling capability of services is important to optimize cloud operating costs and Quality of Service. Therefore, there is a need for auto-scaling technology that is able to dynamically adjust resource allocation to cloud services based on incoming workload. In this paper, we present a predictive auto-scaler for Kubernetes clusters to improve the efficiency of container auto-scaling. Being based on a predictive algorithm, our auto-scaling scheme simplifies the architecture of existing auto-scaling system for more efficient service offerings. In addition, we present experimental evaluation results of our proposed scheme.
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