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

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Bibliographic Details
Published in:	Electronics vol. 14, no. 13 (2025), p. 2626-2651
Main Author:	Al Jawarneh Isam Mashhour
Other Authors:	Rosa, Lorenzo, Venanzi Riccardo, Foschini Luca, Bellavista Paolo
Published:	MDPI AG
Subjects:	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
Online Access:	Citation/Abstract Full Text + Graphics Full Text - PDF
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Description
Abstract:	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
Source:	Advanced Technologies & Aerospace Database