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Softwaretechnik-Trends 43(4) - 2023

https://dl.gi.de/handle/20.500.12116/43225
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Auflistung Softwaretechnik-Trends 43(4) - 2023 nach Autor:in "Eichelberger, Holger"

1 - 3 von 3
  • Konferenzbeitrag
    Analyzing and Improving the Performance of Continuous Container Creation and Deployment
    (Softwaretechnik-Trends Band 43, Heft 4, 2023) Alamoush, Ahmad; Eichelberger, Holger
    Continuous Deployment automates the delivery of new versions of software systems. To ease installation and delivery, often container virtualization is applied. In this paper, we discuss the impact of different (Docker) container image creation techniques for variant-rich Industry 4.0 applications. Our results show that a combination of techniques like container image stacking or semantic fingerprinting can save up to 59% build time and up to 89% deployment time, while not affecting the container startup time.
  • Konferenzbeitrag
    Experiences in Collecting Requirements for an AI-enabled Industry 4.0 Platform
    (Softwaretechnik-Trends Band 43, Heft 4, 2023) Sauer, Christian; Eichelberger, Holger
    Industry 4.0 software platforms target creation, provisioning and operation of industrial applications, e.g., on a shopfloor. Recent advances in Artificial Intelligence (AI), one pillar of Industry 4.0, lead to new demands. The funded project IIP-Ecosphere designs a novel, AI-enabled Industry 4.0 platform. As a basis, we applied two complementing requirements views, namely usage and functional view inspired by IIRA, and collected 67 usage view scenarios and 141 top level functional requirements. In this paper, we summarize our experiences on the requirements collection and discuss their effect on the yet realized platform.
  • Konferenzbeitrag
    Performance comparison of TwinCat ADS for Python and Java
    (Softwaretechnik-Trends Band 43, Heft 4, 2023) Weber, Alexander; Eichelberger, Holger; Schreiber, Per; Wienrich, Svenja
    Real-time and in-process measurements are important in the manufacturing domain, e.g., for real-time process monitoring. For performance reasons, such data is often processed in virtualized environments on edge devices, as e.g., provided by the company Beckhoff. For exploring modern AI methods, integration with high-level languages such as Python or even with Industry 4.0 platforms for advanced data flows is needed. In this paper, we analyze the read/write perfor mance of a Beckhoff device integrated via Python or Java. For our experiments, we use a simulation on a PC as well as a networked setup with a Beckhoff device. We show that the Java-based solution is faster than the Python one by 2-3 times. We also show that small arrays can be read as fast as a single value, that there is no difference between operations for small or big data types and that there is no difference between reading and writing data.