Technology Category
Machine Learning: Learning on the Edge & Model CompressionCognitive Modeling & Cognitive Systems: (Computational) Cognitive ArchitecturesData Mining & Knowledge Management: Scalability, Parallel & Distributed Systems
An Analysis of HPC and Edge Architectures in the Cloud
🤖 AI Summary
This study addresses the practical disparities and co-evolution between high-performance computing (HPC) and edge computing architectures within the cloud continuum. It presents the first large-scale empirical analysis based on 396 real-world, production-grade AWS architectures. Methodologically, we propose a multidimensional, data-driven framework encompassing service topology identification, storage type classification, architectural complexity quantification, and ML service integration statistics. Results reveal systematic differences—and complementary patterns—between HPC and edge architectures across four dimensions: core service composition (e.g., EC2 versus Greengrass/Lambda), storage design paradigms (parallel file systems versus distributed lightweight caches), complexity distributions, and ML embedding strategies. This work delivers the first industry-scale architectural benchmark for the cloud continuum, providing empirically grounded insights and methodological foundations for cross-domain architecture design, resource optimization, and cloud-native convergence of HPC and edge computing.
Technology Category
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Analyze HPC and edge architectures in AWS cloud deployments
Investigate AWS services prevalence and storage systems used
Assess architectural complexity and machine learning services usage
Innovation
Methods, ideas, or system contributions that make the work stand out.
Analyze AWS HPC and edge architectures
Investigate AWS services and storage systems
Assess complexity and machine learning usage
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