Kubernetes Deployment Options for On-Prem Clusters
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866909238175989760 |
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| author | Bryant, Lincoln Gardner, Robert W. Hu, Fengping Jordan, David Taylor, Ryan P. |
| author_facet | Bryant, Lincoln Gardner, Robert W. Hu, Fengping Jordan, David Taylor, Ryan P. |
| contents | Over the last decade, the Kubernetes container orchestration platform has become essential to many scientific workflows. Despite its popularity, deploying a production-ready Kubernetes cluster on-premises can be challenging for system administrators. Many of the proprietary integrations that application developers take for granted in commercial cloud environments must be replaced with alternatives when deployed locally. This article will compare three popular deployment strategies for sites deploying Kubernetes on-premise: Kubeadm with Kubespray, OpenShift / OKD and Rancher via K3S/RKE2. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_01620 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Kubernetes Deployment Options for On-Prem Clusters Bryant, Lincoln Gardner, Robert W. Hu, Fengping Jordan, David Taylor, Ryan P. Computational Physics Distributed, Parallel, and Cluster Computing Over the last decade, the Kubernetes container orchestration platform has become essential to many scientific workflows. Despite its popularity, deploying a production-ready Kubernetes cluster on-premises can be challenging for system administrators. Many of the proprietary integrations that application developers take for granted in commercial cloud environments must be replaced with alternatives when deployed locally. This article will compare three popular deployment strategies for sites deploying Kubernetes on-premise: Kubeadm with Kubespray, OpenShift / OKD and Rancher via K3S/RKE2. |
| title | Kubernetes Deployment Options for On-Prem Clusters |
| topic | Computational Physics Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2407.01620 |