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Actual Resource Usage-Based Container Scheduler for High Resource Utilization
- Issue Date
- Jun-2023
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Keywords
- Cloud computing; Container orchestration; Deep learning; Scheduling; Time series forecasting
- Citation
- Lecture Notes in Electrical Engineering, v.1028 LNEE, pp 611 - 614
- Pages
- 4
- Indexed
- SCOPUS
- Journal Title
- Lecture Notes in Electrical Engineering
- Volume
- 1028 LNEE
- Start Page
- 611
- End Page
- 614
- ISSN
- 1876-1100
1876-1119
- Abstract
- Kubernetes select node and deploy pod based on request to ensure the size of resources for containers with various requirements. In this case, containers are inefficiently managed due to idle resources which are generated by workload configured in various sizes. Therefore, in this study, we propose an Actual Resource Usage-based Scheduler (ARUS), which utilizes the resource usage of each component to perform scheduling to improve the problem of resource waste. ARUS forecasts future resource usage from collected resource usage by utilizing DLinear model. In this case, the optimal node is selected through the scoring for efficient resource utilization (SERU) algorithm. Therefore, ARUS improves resource utilization over conventional kube-scheduler. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Related Researcher
- Jeong, Young Sik
- College of Advanced Convergence Engineering (Department of Computer Science and Artificial Intelligence)