An Analysis of HPC and Edge Architectures in the Cloud

• URL: http://arxiv.org/abs/2508.01494v1
• Date: Sat, 02 Aug 2025 21:32:02 GMT
• Title: An Analysis of HPC and Edge Architectures in the Cloud
• Authors: Steven Santillan, Cristina L. Abad,
• Abstract summary: We analyze a recently published dataset of 396 real-world cloud architectures deployed on AWS.<n>From this dataset, we identify those architectures that contain HPC or edge components and characterize their designs.
• Score: 0.6215404942415159
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Secure Edge Computing Reference Architecture for Data-driven Structural Health Monitoring: Lessons Learned from Implementation and Benchmarking [0.0]
  This paper introduces a scalable and secure edge-computing reference architecture tailored for data-driven Structural Health Monitoring (SHM)<n>Our solution integrates a commercial data acquisition system with off-the-shelf hardware running an open-source edge-computing platform, remotely managed and scaled through cloud services.<n>We study this framework by collecting resource utilization data for machine learning models typically used in SHM applications on two different edge computing hardware platforms.
  arXiv  Detail & Related papers  (2025-03-24T16:33:25Z)
• Multi-dataset synergistic in supervised learning to pre-label structural components in point clouds from shell construction scenes [0.0]
  This study addresses the challenges of segmenting complex structural components in Architecture, Engineering, and Construction (AEC)<n>We establish a baseline through supervised training and a custom validation dataset, evaluate the cross-domain inference with large-scale indoor datasets, and utilize transfer learning to maximize segmentation performance with minimal new data.
  arXiv  Detail & Related papers  (2025-02-20T16:48:14Z)
• ARCH2S: Dataset, Benchmark and Challenges for Learning Exterior Architectural Structures from Point Clouds [0.0]
  This paper introduces a semantically-enriched, photo-realistic 3D architectural models dataset and benchmark for semantic segmentation. It features 4 different building purposes of real-world buildings as well as an open architectural landscape in Hong Kong.
  arXiv  Detail & Related papers  (2024-06-03T14:02:23Z)
• Serving Deep Learning Model in Relational Databases [70.53282490832189]
  Serving deep learning (DL) models on relational data has become a critical requirement across diverse commercial and scientific domains. We highlight three pivotal paradigms: The state-of-the-art DL-centric architecture offloads DL computations to dedicated DL frameworks. The potential UDF-centric architecture encapsulates one or more tensor computations into User Defined Functions (UDFs) within the relational database management system (RDBMS)
  arXiv  Detail & Related papers  (2023-10-07T06:01:35Z)
• Enhancing Architecture Frameworks by Including Modern Stakeholders and their Views/Viewpoints [48.87872564630711]
  The stakeholders with data science and Machine Learning related concerns, such as data scientists and data engineers, are yet to be included in existing architecture frameworks.<n>We surveyed 61 subject matter experts from over 25 organizations in 10 countries.
  arXiv  Detail & Related papers  (2023-08-09T21:54:34Z)
• Data Mesh: a Systematic Gray Literature Review [3.038477115588261]
  Data mesh is an emerging domain-driven decentralized data architecture that aims to minimize or avoid operational bottlenecks. We systematically collected, analyzed, and synthesized 114 industrial gray literature articles. The review provides insights into practitioners' perspectives on the four key principles of data mesh.
  arXiv  Detail & Related papers  (2023-04-03T15:16:46Z)
• Distributed intelligence on the Edge-to-Cloud Continuum: A systematic literature review [62.997667081978825]
  This review aims at providing a comprehensive vision of the main state-of-the-art libraries and frameworks for machine learning and data analytics available today. The main simulation, emulation, deployment systems, and testbeds for experimental research on the Edge-to-Cloud Continuum available today are also surveyed.
  arXiv  Detail & Related papers  (2022-04-29T08:06:05Z)
• Federated Learning with Heterogeneous Architectures using Graph HyperNetworks [154.60662664160333]
  We propose a new FL framework that accommodates heterogeneous client architecture by adopting a graph hypernetwork for parameter sharing. Unlike existing solutions, our framework does not limit the clients to share the same architecture type, makes no use of external data and does not require clients to disclose their model architecture.
  arXiv  Detail & Related papers  (2022-01-20T21:36:25Z)
• Stage-Wise Neural Architecture Search [65.03109178056937]
  Modern convolutional networks such as ResNet and NASNet have achieved state-of-the-art results in many computer vision applications. These networks consist of stages, which are sets of layers that operate on representations in the same resolution. It has been demonstrated that increasing the number of layers in each stage improves the prediction ability of the network. However, the resulting architecture becomes computationally expensive in terms of floating point operations, memory requirements and inference time.
  arXiv  Detail & Related papers  (2020-04-23T14:16:39Z)
• A Privacy-Preserving Distributed Architecture for Deep-Learning-as-a-Service [68.84245063902908]
  This paper introduces a novel distributed architecture for deep-learning-as-a-service. It is able to preserve the user sensitive data while providing Cloud-based machine and deep learning services.
  arXiv  Detail & Related papers  (2020-03-30T15:12:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.