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2025 | Buch

Kapitel lesen Erstes Kapitel lesen

Service-Oriented Computing

18th Symposium and Summer School, SummerSOC 2024, Crete, Greece, June 24–29, 2024, Revised Selected Papers

herausgegeben von: Marco Aiello, Johanna Barzen, Schahram Dustdar, Frank Leymann

Verlag: Springer Nature Switzerland

Buchreihe : Communications in Computer and Information Science

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

Inhaltsverzeichnis
insite
SUCHEN

Über dieses Buch

This book constitutes the refereed proceedings of the 18th Symposium and Summer School on Service-Oriented Computing, SummerSOC 2024, held in Crete, Greece, during June 24–29, 2024.

The 8 revised full papers and 1 short paper presented in these proceedings were carefully reviewed and selected from 24 submissions. They cover the following topics: modeling the digital world; quantum computing; data platforms.

Inhaltsverzeichnis

Frontmatter

Modeling the Digital World

Frontmatter
API-Driven Cloud-Edge Orchestration with PULCEO: A Proof of Concept
Abstract
Over the years, many solutions have emerged for performing service placement decisions in cloud-edge systems to optimize response time, energy usage, or cost. However, most of the solutions are theoretically evaluated or by using simulators. To tackle this issue, we conceptually designed and implemented PULCEO (Platform for Universal and Lightweight Cloud-Edge Orchestration) to contribute a platform for holistically creating, operating, monitoring, evaluating, and documenting cloud-edge orchestration environments and experimental evaluations. We used our platform to realize a representative sample topology that integrates on-premises and cloud resources. We also obtained monitoring data and performed service placement operations to analyze the application response time as an essential metric in such environments. The evaluation shows that our platform can fulfill essential requirements for cloud-edge orchestration, especially creating topologies with configurable monitoring and support for dynamically obtaining link quality metrics.
Sebastian Böhm, Guido Wirtz
An Orchestrator for the Dynamic Extension of Automotive E/E Architectures to the Cloud
Abstract
Service orientation finds its way into automotive electrical/electronic (E/E) architectures. The greater flexibility and adaptability of a service-oriented architecture (SOA) requires a central module, called an orchestrator, which manages the communication structure and updates the available functions. Such a software orchestrator in the automotive sector is part of scientific research, especially because of the strict safety and availability requirements of automated and connected vehicles.
In this paper, our own requirements for an orchestrator are compared with existing requirements and implementations from the scientific community. This leads to a focus of this work on extending the E/E architecture into the cloud to produce a control-over-the-air (COTA) approach. Such an approach is implemented using a non-safety-critical function and carried out on the institute’s own demonstrator platform representing a basic modern E/E architecture with cloud connection. The resulting orchestrator is able to dynamically switch between different controllers, one deployed in the vehicle and one in the cloud, and close the loop of a heating, ventilation and air conditioning (HVAC) control system of a city bus passenger compartment model executed on our demonstrator platform. Finally, we compare and evaluate the requirements that we have identified as open in the community to see if they have been implemented.
Martin Sommer, Houssem Guissouma, Marc Schindewolf, Eric Sax
Integrating Artifact Translation Into Model Transformation Processes
Abstract
Model transformation is a critical aspect of modern software engineering, enabling the effective development, maintenance, and evolution of software systems while improving their quality, correctness, and interoperability. However, one issue arises when a model under transformation references artifacts such as source code files or binary executables: Model transformations typically do not consider the referenced artifacts, resulting in the possibility of them becoming incompatible with the final model after transformation. As a result, the referenced artifacts often require manual adaptations which can be complex, time-consuming, and error-prone. To address this issue, we present an approach that integrates artifact translation mechanisms into the model transformation process. In addition to the translation between different concrete artifacts during model transformation, our approach also enables the use of abstract artifacts in models in order to translate them into concrete artifacts during model refinement, which is a special type of model transformation. To validate the practical feasibility of our approach, we present a prototype and two case studies in the domain of quantum software engineering.
Daniel Vietz, Johanna Barzen, Lukas Harzenetter, Frank Leymann, Benjamin Weder
Evaluating Cloud-Native Deployment Options with a Focus on Reliability Aspects
Abstract
For deploying applications in the cloud various options exist that can be compared based on their impacts on different quality attributes. In this work, we evaluate such deployment options with a focus on the quality aspect of reliability. More specifically, we consider characteristics relevant to reliability in the context of so-called cloud-native applications. The chosen characteristics stem from a quality model for cloud-native applications. By implementing different options to realize these characteristics in an exemplary application on AWS, we make them comparable. This evaluation is then used to extend the modeling approach for the quality model so that the different implementation options can be represented and evaluated using the quality model. Our contribution therefore is an investigation of deployment options for cloud-native applications considering reliability, as well as its transfer into an existing quality evaluation approach for further experimentation.
Franka Knoch, Robin Lichtenthäler, Guido Wirtz
Towards a Taxonomy of Infrastructure as Code Misconfigurations: An Ansible Study
Abstract
Infrastructure as Code (IaC) enables the management and provisioning of infrastructure using code instead of manual processes. While the IaC approach can simplify and automate infrastructure management, the configuration errors (i.e., misconfigurations) in the IaC scripts can significantly hinder achieving the benefits of the automation, incur undue costs, and leave the system faulty and insecure. In this paper, we present a taxonomy of IaC misconfigurations to assist practitioners and researchers in building tools for detecting misconfigurations. The user manuals of the IaC frameworks describe configuration parameters and their usage constraints, and provide recommendations on configuring certain parameters correctly. Hence, we systemically collected and analyzed 100 user manuals of the Ansible IaC language to compile a catalog of 25 configuration errors. We also developed a proof-of-concept tool for using the information in the user manuals to generate misconfiguration detection rules.
Roya Nasiri, Indika Kumara, Damian Andrew Tamburri, Willem-Jan van den Heuvel

Quantum Computing

Frontmatter
Minimial-Risk Training Samples for QNN Training from Measurements
Abstract
By using Quantum Neural Networks (QNNs), the principles of quantum computing can be employed to perform supervised learning on quantum computers. Herein, a unitary transformation is trained using sets of quantum input states and their associated outputs. When the exact output states of the transformation are known, recent results show that entanglement can drastically reduce the approximation error of a QNN, without increasing the number of required training samples. However, the exact output states might not be readily available. In certain scenarios, only the measurement outcomes of these quantum states after measurement with an observable are available. Therefore, this work investigates the effect of entangled training samples when training using measurement outcomes. For observables described by one-dimensional projectors, we specify entangled training samples that minimize the approximation error. Furthermore, we validate our findings on a simulator and show that when using entangled training samples, the approximation error depends on the factorization of the entangled samples.
Alexander Mandl, Johanna Barzen, Marvin Bechtold, Frank Leymann
Exploring the Cost Landscape of Variational Quantum Algorithms
Abstract
Variational Quantum Algorithms (VQAs) have emerged as a promising approach to leverage the capabilities of quantum computing, even within the constraints of limited qubits and noise. Understanding their iterative process, including their cost landscapes, is necessary to optimize these algorithms. This landscape represents the interplay between the algorithm’s parameters and the cost function, offering a visualization of the challenges for the optimization process. Regions known as barren plateaus and narrow gorges can impede optimization algorithms by causing gradients to vanish, leading to stalled optimization processes. Recognizing and devising strategies to circumvent these severe problems is essential for designing VQAs. For this purpose, we provide an overview of local and global metrics to support the understanding of the VQA cost landscape. Moreover, our results may serve as a baseline for further research on cost landscapes.
Lavinia Stiliadou, Johanna Barzen, Frank Leymann, Alexander Mandl, Benjamin Weder

Data Platforms

Frontmatter
A Service-Based Pipeline for Complex Linguistic Tasks Adopting LLMs and Knowledge Graphs
Abstract
This paper introduces a microservices-based architecture designed for executing complex linguistic tasks using Large Language Models (LLMs) and Knowledge Graphs (KGs). It has been conceived by focusing on the legal domain, and it integrates Domain-specific KGs and Constraint KGs to address tasks such as law extraction and reasoning. We outline how the pipeline works through a running example involving the extraction of legislative references from legal documents. Furthermore, we discuss a methodology for building KGs from unstructured documents and employing zero-shot prompt engineering techniques to facilitate information extraction. Finally, we present a validation process leveraging the Constraint KG to ensure the coherence and correctness of generated outputs.
Filippo Bianchini, Marco Calamo, Francesca De Luzi, Mattia Macrì, Massimo Mecella
Design and Implementation of a High Performance Domain Name Service on Commodity Hardware
Abstract
The internet Domain Name System (DNS) is one of the essential components of the World Wide Web and the whole internet. The core concept is a large distributed and hierarchical database that translates internet domain names into host addresses and other supplemental information. Name servers can serve millions of domains and usually have to answer thousands of requests per second. Because of their vital function for any internet service, domain name servers are regularly target of Denial-of-Service attacks, where millions of queries per second are used to overload the database system. This work describes an approach on how to achieve answer rates in this dimension with a single server built from low-cost commodity hardware.
Florian Heinz, Martin Kluge
Backmatter
Metadaten
Titel
Service-Oriented Computing
herausgegeben von
Marco Aiello
Johanna Barzen
Schahram Dustdar
Frank Leymann
Copyright-Jahr
2025
Electronic ISBN
978-3-031-72578-4
Print ISBN
978-3-031-72577-7
DOI
https://doi.org/10.1007/978-3-031-72578-4

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