ARC-V: Vertical Resource Adaptivity for HPC Workloads in Containerized Environments
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866916720396992512 |
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| author | Medeiros, Daniel Williams, Jeremy J. Wahlgren, Jacob Leite, Leonardo Saud Maia Peng, Ivy |
| author_facet | Medeiros, Daniel Williams, Jeremy J. Wahlgren, Jacob Leite, Leonardo Saud Maia Peng, Ivy |
| contents | Existing state-of-the-art vertical autoscalers for containerized environments are traditionally built for cloud applications, which might behave differently than HPC workloads with their dynamic resource consumption. In these environments, autoscalers may create an inefficient resource allocation. This work analyzes nine representative HPC applications with different memory consumption patterns. Our results identify the limitations and inefficiencies of the Kubernetes Vertical Pod Autoscaler (VPA) for enabling memory elastic execution of HPC applications. We propose, implement, and evaluate ARC-V. This policy leverages both in-flight resource updates of pods in Kubernetes and the knowledge of memory consumption patterns of HPC applications for achieving elastic memory resource provisioning at the node level. Our results show that ARC-V can effectively save memory while eliminating out-of-memory errors compared to the standard Kubernetes VPA. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_02964 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ARC-V: Vertical Resource Adaptivity for HPC Workloads in Containerized Environments Medeiros, Daniel Williams, Jeremy J. Wahlgren, Jacob Leite, Leonardo Saud Maia Peng, Ivy Distributed, Parallel, and Cluster Computing Existing state-of-the-art vertical autoscalers for containerized environments are traditionally built for cloud applications, which might behave differently than HPC workloads with their dynamic resource consumption. In these environments, autoscalers may create an inefficient resource allocation. This work analyzes nine representative HPC applications with different memory consumption patterns. Our results identify the limitations and inefficiencies of the Kubernetes Vertical Pod Autoscaler (VPA) for enabling memory elastic execution of HPC applications. We propose, implement, and evaluate ARC-V. This policy leverages both in-flight resource updates of pods in Kubernetes and the knowledge of memory consumption patterns of HPC applications for achieving elastic memory resource provisioning at the node level. Our results show that ARC-V can effectively save memory while eliminating out-of-memory errors compared to the standard Kubernetes VPA. |
| title | ARC-V: Vertical Resource Adaptivity for HPC Workloads in Containerized Environments |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2505.02964 |