An Analysis of HPC and Edge Architectures in the Cloud
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909719105372160 |
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| author | Santillan, Steven Abad, Cristina L. |
| author_facet | Santillan, Steven Abad, Cristina L. |
| contents | We analyze a recently published dataset of 396 real-world cloud architectures deployed on AWS, from companies belonging to a wide range of industries. From this dataset, we identify those architectures that contain HPC or edge components and characterize their designs. Specifically, we investigate the prevalence and interplay of AWS services within these architectures, examine the types of storage systems employed, assess architectural complexity and the use of machine learning services, discuss the implications of our findings and how representative these results are of HPC and edge architectures in the cloud. This characterization provides valuable insights into current industry practices and trends in building robust and scalable HPC and edge solutions in the cloud continuum, and can be valuable for those seeking to better understand how these architectures are being built and to guide new research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_01494 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An Analysis of HPC and Edge Architectures in the Cloud Santillan, Steven Abad, Cristina L. Distributed, Parallel, and Cluster Computing Software Engineering We analyze a recently published dataset of 396 real-world cloud architectures deployed on AWS, from companies belonging to a wide range of industries. From this dataset, we identify those architectures that contain HPC or edge components and characterize their designs. Specifically, we investigate the prevalence and interplay of AWS services within these architectures, examine the types of storage systems employed, assess architectural complexity and the use of machine learning services, discuss the implications of our findings and how representative these results are of HPC and edge architectures in the cloud. This characterization provides valuable insights into current industry practices and trends in building robust and scalable HPC and edge solutions in the cloud continuum, and can be valuable for those seeking to better understand how these architectures are being built and to guide new research. |
| title | An Analysis of HPC and Edge Architectures in the Cloud |
| topic | Distributed, Parallel, and Cluster Computing Software Engineering |
| url | https://arxiv.org/abs/2508.01494 |