International journal of advanced smart convergence

The Institute of Internet, Broadcasting and Communication (한국인터넷방송통신학회)
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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)
  • Received : 2023.10.12
  • Accepted : 2023.10.23
  • Published : 2023.12.31
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Abstract

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.

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Acknowledgement

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

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