DOI QR코드

DOI QR Code

An Engine for DRA in Container Orchestration Using Machine Learning

  • Gun-Woo Kim (Department of Computer Science, Kwangwoon University) ;
  • Seo-Yeon Gu (Department of Computer Science, Kwangwoon University) ;
  • Seok-Jae Moon (Department of Artificial Intelligence Institute of Information Technology, KwangWoon University) ;
  • Byung-Joon Park (Department of Computer Science, Kwangwoon University)
  • 투고 : 2023.10.12
  • 심사 : 2023.10.23
  • 발행 : 2023.12.31

초록

Recent advancements in cloud service virtualization technologies have witnessed a shift from a Virtual Machine-centric approach to a container-centric paradigm, offering advantages such as faster deployment and enhanced portability. Container orchestration has emerged as a key technology for efficient management and scheduling of these containers. However, with the increasing complexity and diversity of heterogeneous workloads and service types, resource scheduling has become a challenging task. Various research endeavors are underway to address the challenges posed by diverse workloads and services. Yet, a systematic approach to container orchestration for effective cloud management has not been clearly defined. This paper proposes the DRA-Engine (Dynamic Resource Allocation Engine) for resource scheduling in container orchestration. The proposed engine comprises the Request Load Procedure, Required Resource Measurement Procedure, and Resource Provision Decision Procedure. Through these components, the DRA-Engine dynamically allocates resources according to the application's requirements, presenting a solution to the challenges of resource scheduling in container orchestration.

키워드

과제정보

This work is financially supported by Korea Ministry of Environment(MOE) Graduate School specialized in Integrated Pollution Prevention and Control Project.

참고문헌

  1. A. M. Potdar, N. D G, S. Kengond, and M. M. Mulla, "Performance Evaluation of Docker Container and Virtual Machine," Procedia Computer Science, vol. 171, pp. 1419-1428, Jun 2020, DOI: https://doi.org/10.1016/j.procs.2020.04.152 
  2. C. Carrion, "Kubernetes Scheduling: Taxonomy, ongoing issues and challenges," ACM Computing Surveys, vol.55, pp. 1-37, July 2023, DOI: https://doi.org/10.1145/3539606 
  3. Mohit Kumar, S.C. Sharma, Anubhav Goel, S.P. Singh, "A comprehensive survey for scheduling techniques in cloud computing", Journal of Network and Computer Application, vol. 143, pp. 1-33, June 2019, DOI: https://doi.org/10.1016/j.jnca.2019.06.006 
  4. D. Saxena, J. Kumar, Ashutosh Kumar Singh, and S. Schmid, "Performance Analysis of Machine Learning Centered Workload Prediction Models for Cloud," IEEE Transcations on Parallel and Distributed Systems, vol. 34, no. 4, pp. 1313-1330, April 2023, DOI: https://doi.org/10.1109/tpds.2023.3240567 
  5. Z. Zhong, M. Xu, M. A. Rodriguez, C. Xu, and R. Buyya, "Machine Learning-based Orchestration of Containers: A Taxonomy and Future Directions," ACM Computing Surveys, vol. 54, no.217, pp. 1-35, Sep 2022, DOI: https://doi.org/10.1145/3510415 
  6. P. V. de Campos Souza, "Fuzzy neural networks and neuro-fuzzy networks: A review the main techniques and applications used in the literature," Applied Soft Computing, vol. 92, p. 106275, July 2020, DOI: https://doi.org/10.1016/j.asoc.2020.106275 
  7. Arabinda Pradhan, Sukant Kishoro Bisoy, Amardeep Das, "A survey on PSO based meta-heuristic scheduling mechanism in cloud computing environment," Journal of King Saud University - Computer and Information Sciences, vol. 34, pp. 4888-4901, Jan 2022 DOI: https://doi.org/10.1016/j.jksuci.2021.01.003 
  8. Yogesh Kumar, Surabhi Kaul, Yu-Chen Hu, "Machine learning for energy-resource allocation, workflow scheduling and live migration in cloud computing: State-of-the-art survey," Sustainable Computing: Informatics and Systems, vol. 36, p. 100780, Jan 2022 DOI: https://doi.org/10.1016/j.suscom.2022.100780