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Efficient Parallel Processing of Big Data on Supercomputers for Industrial IoT Environments

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Publicado en:Electronics vol. 14, no. 13 (2025), p. 2626-2651
Autor Principal: Al Jawarneh Isam Mashhour
Outros autores: Rosa, Lorenzo, Venanzi Riccardo, Foschini Luca, Bellavista Paolo
Publicado:
MDPI AG
Materias:
Parallel processing
Software
Data processing
Performance evaluation
Big Data
Industry 4.0
Edge computing
Batch processing
Manufacturing
Workloads
High performance computing
Internet of Things
Factories
Data analysis
Simulation
Cluster analysis
Digital twins
Clustering
Sensors
Industrial applications
Energy efficiency
Paradigms
Real time
Vector quantization
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Resumo:The integration of distributed big data analytics into modern industrial environments has become increasingly critical, particularly with the rise of data-intensive applications and the need for real-time processing at the edge. While High-Performance Computing (HPC) systems offer robust petabyte-scale capabilities for efficient big data analytics, the performance of big data frameworks, especially on ARM-based HPC systems, remains underexplored. This paper presents an extensive experimental study on deploying Apache Spark 3.0.2, the de facto standard in-memory processing system, on an ARM-based HPC system. This study conducts a comprehensive performance evaluation of Apache Spark through representative big data workloads, including K-means clustering, to assess the effects of latency variations, such as those induced by network delays, memory bottlenecks, or computational overheads, on application performance in industrial IoT and edge computing environments. Our findings contribute to an understanding of how big data frameworks like Apache Spark can be effectively deployed and optimized on ARM-based HPC systems, particularly when leveraging vectorized instruction sets such as SVE, contributing to the broader goal of enhancing the integration of cloud–edge computing paradigms in modern industrial environments. We also discuss potential improvements and strategies for leveraging ARM-based architectures to support scalable, efficient, and real-time data processing in Industry 4.0 and beyond.
ISSN:2079-9292
DOI:10.3390/electronics14132626
Fonte:Advanced Technologies & Aerospace Database