Name
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ictc46691.2019.8939932 | 2019 10TH INTERNATIONAL CONFERENCE ON INFORMATION AND COMMUNICATION TECHNOLOGY CONVERGENCE (ICTC): ICT CONVERGENCE LEADING THE AUTONOMOUS FUTURE |
Keywords | Field | DocType |
Microservices, Container, Auto-Scaling, Cloudnative Application | Architecture,Social media,Workload,Computer science,Quality of service,Resource allocation,Microservices,Scaling,Distributed computing,Cloud computing | Conference |
ISSN | Citations | PageRank |
2162-1233 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hanqing Zhao | 1 | 4 | 5.83 |
| Hyunwoo Lim | 2 | 0 | 0.34 |
| Muhammad Hanif | 3 | 0 | 0.34 |
| choonhwa lee | 4 | 434 | 44.98 |