과제정보
이 논문은 2023년도 정부(과학기술정보통신부)의 재원으로 정보통신기획평가원의 지원을 받아 수행된 연구임(No.2022-0-01198, 융합보안대학원(고려대학교)). 본 연구는 과학기술정보통신부 및 정보통신기획평가원의 ICT혁신인재4.0사업의 연구결과로 수행되었음(IITP-2023-RS-2022-00156439).
참고문헌
- A. R. Setlur, S. J. Nirmala, H. S. Singh, and S. Khoriya, "An efficient fault tolerant workflow scheduling approach using replication heuristics and checkpointing in the cloud," Journal of Parallel and Distributed Computing, Vol.136, pp.14-28, 2020. https://doi.org/10.1016/j.jpdc.2019.09.004
- C. Yang, Q. Huang, Z. Li, K. Liu, and F.Hu, "Big Data and cloud computing: innovation opportunities and challenges," International Journal of Digital Earth, Vol.10, No.1, pp.13-53, 2017. https://doi.org/10.1080/17538947.2016.1239771
- A. Das, P Rad, K. K. R. Choo, B. Nouhi, J. Nish and J. Martel, "Distributed machine learning cloud teleophthalmology IoT for predicting AMD disease progression," Future Generation Computer Systems, Vol.93, pp.486-498, 2019. https://doi.org/10.1016/j.future.2018.10.050
- B. Liu, J. Li, W. Lin, W. Bai, P. Li, and Q. Gao, "K-PSO: An improved PSO-based container scheduling algorithm for big data applications," International Journal of Network Management, Vol.31, No.2, pp.e2092, 2021.
- M. Niu, B. Cheng, Y. Feng, and J. Chen, "GMTA: A geoaware multi-agent task allocation approach for scientific workflows in container-based cloud," IEEE Transactions on Network and Service Management, Vol.17, No.3, pp.1568-1581, 2020.
- I. Altintas et al., "Workflow-driven distributed machine learning in CHASE-CI: A cognitive hardware and software ecosystem community infrastructure." IEEE international parallel and distributed processing symposium workshops, pp.865-873, 2019.
- Q. Zhang, L. Liu, C. Pu, Q. Dou, L. Wu, and W. Zhou, "A comparative study of containers and virtual machines in big data environment." IEEE 11th International Conference on Cloud Computing, pp.178-185, 2018.
- M. Malinverno, J. Mangues-Bafalluy, C. E. Casetti, C. F. Chiasserini, M. Requena-Esteso, and J. Baranda, "An EdgeBased Framework for Enhanced Road Safety of Connected Cars," IEEE Access, Vol.8, pp.58018-58031, 2020.
- V. K. Vavilapalli et al., "Apache hadoop yarn: Yet another resource negotiator," Proceedings of the 4th annual Symposium on Cloud Computing. pp.1-16, 2013.
- Swarmkit [Internet], https://github.com/moby/swarmkit.
- Docker-swarm [Internet], https://github.com/docker-archive/classicswarm.
- kubernetes [Internet], https://github.com/kubernetes/kubernetes.
- V. M. Bhasi, J. R. Gunasekaran, P. Thinakaran, C. S. Mishra, M. T. Kandemir, and C. Das, "Kraken: Adaptive container provisioning for deploying dynamic dags in serverless platforms," Proceedings of the ACM Symposium on Cloud Computing. pp.153-167, 2021.
- R. Nakazawa, K. Ogata, S. Seelam, and T. Onodera, "Taming performance degradation of containers in the case of extreme memory overcommitment," IEEE 10th International Conference on Cloud Computing, pp.196-204, 2017.
- W. Chen, A. Pi, S. Wang, and X. Zhou, "Pufferfish: Container-driven elastic memory management for dataintensive applications," Proceedings of the ACM Symposium on Cloud Computing, pp.259-271, 2019.
- Kubernetes Components [Internet], https://kubernetes.io/docs/concepts/overview/components.
- Docker [Internet], https://www.docker.com.
- Containerd [Internet], https://containerd.io.
- Container Runtime Interface [Internet], https://kubernetes.io/docs/concepts/architecture/cri.
- D. Williams, H. Jamjoom, Y. H. Liu, H. Weatherspoon, "Overdriver: Handling memory overload in an oversubscribed cloud," ACM SIGPLAN Notices, Vol.46, No.7 pp.205-216, 2011. https://doi.org/10.1145/2007477.1952709
- J. Dogani, R. Namvar, F. Khunjush, "Auto-scaling techniques in container-based cloud and edge/fog computing: Taxonomy and survey," Computer Communications, Vol.209, pp.120-150, 2023. https://doi.org/10.1016/j.comcom.2023.06.010
- C Carrion, "Kubernetes scheduling: Taxonomy, ongoing issues and challenges," ACM Computing Surveys, Vol.55, No.7, pp.1-37, 2022. https://doi.org/10.1145/3539606
- L. M. Ruiz, P. P. Pueyo, J. Mateo-Fornes, J. V. Mayoral, and F. S. Tehas, "Autoscaling pods on an on-premise kubernetes infrastructure qos-aware," IEEE Access, Vol.10, pp.33083-33094, 2022.
- F. Zhang, X. Tang, X. Li, S. U. Khan, and Z. Li, "Quantifying cloud elasticity with container-based autoscaling," Future Generation Computer Systems, Vol.98, pp.672-681, 2019. https://doi.org/10.1016/j.future.2018.09.009
- Y. Sfakianakis, M. Marazakis, and A. Bilas, "Skynet: Performance-driven resource management for dynamic workloads, " IEEE 14th International Conference on Cloud Computing, pp.527-539, 2021.
- N. D. Nguyen, L. A, Phan, D. H. Park, S. Kim, and T. Kim, "ElasticFog: Elastic resource provisioning in container-based fog computing," IEEE Access, Vol.8 pp.183879-183890, 2020.
- Y. Al-Dhuraibi, F. Paraiso, N. Djarallah, and P. Merle, "Autonomic vertical elasticity of docker containers with elasticdocker," IEEE 10th International Conference on Cloud Computing, pp.472-479, 2017.
- G. Rattihalli, M. Govindaraju, H. Lu, and D. Tiwari, "Exploring potential for non-disruptive vertical auto scaling and resource estimation in kubernetes," IEEE 12th International Conference on Cloud Computing, 2019.
- "clinet-go", [Internet], https://github.com/kubernetes/client-go.
- Kubernetes Jobs, [Internet], https://kubernetes.io/docs/concepts/workloads/controllers/job.