Fraunhofer Institute for Software and Systems EngineeringWith increasing connectivity, digitization, and data intensity, the demands on distributed and parallel computing systems in companies and organizations are growing. The efficient processing of large amounts of data in real time, the flexible scaling of resources, and the assurance of performance and energy efficiency are key challenges that conventional IT infrastructures are often unable to meet.
Distributed systems and parallel computing
Research on edge cloud continuum, distributed learning, and scalable computing environments.
Our research area develops innovative approaches for the edge-cloud continuum that enable seamless distribution and processing of data and workloads across different locations and infrastructures. The focus is on dynamic resource management, energy-efficient parallelization, the development of scalable architectures for industrial applications, and the integration of AI and data management tools into distributed systems. We support companies in implementing modern, distributed systems that enable flexible and efficient use of computing resources while reducing operating costs. Our research includes the development of new algorithms, frameworks, and tools for the management and optimization of distributed systems, the performance of performance analyses, and the support of pilot projects in various industries. Through practical research and close cooperation with partners from industry and academia, we contribute to making digital infrastructures future-proof, sustainable, and powerful.
Our solutions enable companies to fully exploit the potential of digitalization, implement innovative data-driven business models, and position themselves successfully in the competitive environment. The results of our work show that the use of modern distributed systems can significantly improve scalability, reliability, and energy efficiency.
Latest publications
| Jahr Year | Titel/Autor:in Title/Author | Publikationstyp Publication Type |
|---|---|---|
| 2026 | International Strategies Toward the Data Economy Jussen-Lengersdorf, Ilka; Otto, Boris; Emons, Sebastian; Parker, Geoffrey G.; Chang, Boyoon |
Paper |
| 2026 | Designing a Neural Question-Answering System for Times of (Information) Pandemics Graf, Johannes; Lancho, Gino; Heinrich, Kai; Möller, Frederik; Schoormann, Thorsten; Zschech, Patrick |
Zeitschriftenaufsatz Journal Article |
| 2025 | Towards Improved Data Quality Management Tools in Logistics Lehmann, Lukas; Tebernum, Daniel; Klann, Timon Sebastian; Kirchheim, Alice |
Zeitschriftenaufsatz Journal Article |
| 2025 | Strategic Open Source in Industrial Digital Ecosystems Schleimer, Anna Maria; Otto, Boris; Hirschen, Christian; Oelsner, Tom |
Paper |
| 2025 | Evaluation of a large language model to simplify discharge summaries and provide cardiological lifestyle recommendations Rust, Paul; Frings, Julian; Meister, Sven; Fehring, Leonard |
Zeitschriftenaufsatz Journal Article |
| 2025 | Proposing an Integrated Process Model for Designing Clinical Decision Support Tools Wolf, Eduard; Morisse, Karsten; Meister, Sven |
Konferenzbeitrag Conference Paper |
Diese Liste ist ein Auszug aus der Publikationsplattform Fraunhofer-Publica
This list has been generated from the publication platform Fraunhofer-Publica
Examples from our projects
Our research projects are developed in close collaboration with industry partners. They are market- and demand-oriented, with the aim of providing applicable solutions for the cloud edge ecosystem.
"ECC4P": Optimizing manufacturing processes through secure data exchange and AI-supported sensor data analysis
The Fraunhofer project "Edge Cloud Continuum for Production" (ECC4P) uses data space technologies for the secure exchange of data between the edge and the cloud to optimize the manufacturing process of workpieces in production. Sensors monitor the manufacturing process on machine tools. By evaluating their data in a cloud, specialized ML models are generated that then optimize the control of the machines. This significantly reduces the reject rate and prevents machine downtime as much as possible. The parallel project "SoundTrack" uses the same scenario within the quality control of workpieces using newly developed sensors on a test bench for workpieces.
IPCEI-CIS Sustainability-Focused Orchestration in the Edge Cloud Continuum
In the project “IPCEI-CIS Sustainability-Focused Orchestration in the Edge Cloud Continuum,” T-Systems International GmbH and the Fraunhofer-Gesellschaft are collaborating to integrate energy efficiency into the entire technology environment of the Edge Cloud Continuum. The goal is to prioritize energy-efficient practices from infrastructure to application development and Edge IoT setups to promote a sustainable and environmentally friendly technology landscape.
FEC-ARRC: Optimization of the Fraunhofer Edge Cloud with automatic recommendations for resource configuration
ARRC optimizes the Fraunhofer Edge Cloud through seamless OpenStack integration, explainable AI, and automated rightsizing via GitOps. In the FEC-ARRC project, we are integrating the Automatic Recommender for Resource Configuration, or ARRC for short, into the OpenStack ecosystem of the Fraunhofer Edge Cloud (FEC). The FEC is a productively operated reference system that shows companies how edge and cloud platforms work in everyday life. ARRC uses explainable artificial intelligence, time series analysis, and multi-agent game theory methods. This enables the system to continuously and decentralizedly evaluate the utilization and configuration of the platform, make rule-compliant recommendations on the correct size of resources, and automatically implement approved changes using the GitOps operating model.