Cloud Orchestration with Kubernetes/Docker

Authors

  • Ravi Teja Avireneni Industrial Management, University of Central Missouri, USA. Author
  • Sri Harsha Koneru Computer Information Systems and Information Technology, University of Central Missouri, USA. Author
  • Naresh Kiran Kumar Reddy Yelkoti Information Systems Technology and Information Assurance, Wilmington University, USA. Author
  • Sivaprasad Yerneni Khaga Environmental Engineering, University of New Haven, USA. Author
  • Sanketh Nelavelli Computer Science Technology, Texas A&M University, USA. Author

DOI:

https://doi.org/10.63282/3117-5481/AIJCST-V4I1P103

Keywords:

Cloud Orchestration, Kubernetes, Docker Swarm, Containerisation, AI Workloads, ML Pipelines, Scalability, Resource Utilisation, Fault Tolerance, Autoscaling, Hybrid Cloud, Multi-Cloud, Microservices

Abstract

The increasing adoption of artificial intelligence (AI) workloads has placed significant demands on cloud-native infrastructure, particularly in terms of scalability, resource isolation, and automated management of containerised services. Container orchestration platforms such as Kubernetes and Docker have thereby become critical enablers for deploying AI/ML pipelines at scale. For example, research shows that Kubernetes is effective for container orchestration in AI cloud environments (Lokiny, 2022). Additionally, machine-learning–based orchestration frameworks for containers have been explored to improve scheduling, allocation and performance (Zhong et al., 2021). Yet, despite these advances, there remains a paucity of comparative analysis focused on AI workloads especially those that contrast orchestration platforms in hybrid or multi-cloud settings, and that evaluate metrics such as latency, throughput, fault-tolerance, and cost-efficiency. This paper presents a systematic comparative study of Docker- and Kubernetes-based orchestration frameworks for AI workloads, employing a multi-factor benchmark across scalability, resource utilisation, fault resilience, and operational cost. The experimental setup utilises micro-service and deep-learning inference pipelines deployed via Docker Swarm and Kubernetes across public cloud infrastructure. Results indicate that Kubernetes outperforms Docker Swarm in horizontal scaling and fault resilience, while Docker Swarm demonstrates marginal benefits in simplicity of deployment and lower management overhead in small-scale scenarios. Furthermore, the cost-performance trade-offs reveal that orchestration maturity and autoscaling policies favour Kubernetes when workloads grow beyond moderate scale. The paper discusses the implications for AI DevOps teams and cloud architects, offering guidelines for selecting and configuring orchestration technologies aligned with AI workload characteristics. In conclusion, as AI workloads continue shifting toward containerised, distributed, and hybrid-cloud environments, the orchestration strategy plays a pivotal role in ensuring performance, reliability, and cost-efficiency of the underlying infrastructure

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Published

2022-01-11

Issue

Vol. 4 No. 1 (2022)

Section

Articles

How to Cite

[1]
R. T. Avireneni, S. H. Koneru, N. K. K. Reddy Yelkoti, S. Y. Khaga, and S. Nelavelli, “Cloud Orchestration with Kubernetes/Docker”, AIJCST, vol. 4, no. 1, pp. 24–34, Jan. 2022, doi: 10.63282/3117-5481/AIJCST-V4I1P103.

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