Digital Twin Data Management: Framework and Performance Metrics of Cloud-Based ETL System

Abstract

This study delves into the EA-SAS platform, a digital twin environment developed by our team, with a particular focus on the EA-SAS Cloud Scheduler, our bespoke program designed to optimize ETL (extract, transform, and load) scheduling and thereby enhance automation within industrial systems. We elucidate the architectural intricacies of the EA-SAS Cloud Scheduler, demonstrating its adeptness in efficiently managing computationally heavy tasks, a capability underpinned by our empirical benchmarks. The architecture of the scheduler incorporates Docker to create isolated task environments and leverages RabbitMQ for effective task distribution. Our analysis reveals the EA-SAS Cloud Scheduler’s prowess in maintaining minimal overhead times, even in scenarios characterized by high operational loads, underscoring its potential to markedly bolster operational efficiency in industrial settings. While acknowledging the limitations inherent in our current assessment, particularly in simulating real-world industrial complexities, the study also charts potential future research pathways. These include a thorough exploration of the EA-SAS Cloud Scheduler’s adaptability across diverse industrial scenarios and an examination of the integration challenges associated with its reliance on specific technological frameworks.

Enhancing Industrial Process Control: Integrating Intelligent Digital Twin Technology with Proportional-Integral-Derivative Regulators

“…It necessitates comprehensive data analysis, including the mathematical modeling of physical systems and processes, forecasting, and applying statistical algorithms. Digital twin uses fresh monitoring data to represent the real-time state of the system and to estimate the future state [38]. For instance, the integration of digital twins across various industries has demonstrated significant benefits, including reduced operational costs, enhanced productivity, and superior decision-making capabilities [39].…”

“…The framework consists of the user entity for hosting software systems and interfaces, the digital twin entity for the digital representation and synchronization of observable manufacturing elements (OMEs), and the device communication entity for data interaction and device control. Each entity is further divided into sub-entities and functional entities, such as the data collection sub-entity for data acquisition and pre-processing and the device control sub-entity for actuation and operational control [38].…”