@inproceedings{5405d1cca9334ec2a6ac4cb41063bfef,
title = "Actual Resource Usage-Based Container Scheduler for High Resource Utilization",
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.",
keywords = "Cloud computing, Container orchestration, Deep learning, Scheduling, Time series forecasting",
author = "Sihyun Park and Jueun Jeon and Byeonghui Jeong and Kyuwon Park and Seungyeon Baek and Jeong, {Young Sik}",
note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 14th International Conference on Computer Science and its Applications, CSA 2022 and the 16th KIPS International Conference on Ubiquitous Information Technologies and Applications, CUTE 2022 ; Conference date: 19-12-2022 Through 21-12-2022",
year = "2023",
doi = "10.1007/978-981-99-1252-0_82",
language = "English",
isbn = "9789819912513",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "611--614",
editor = "Park, {Ji Su} and Yang, {Laurence T.} and Yi Pan and Yi Pan and Park, {Jong Hyuk}",
booktitle = "Advances in Computer Science and Ubiquitous Computing - Proceedings of CUTE-CSA 2022",
address = "Germany",
}