Fraunhofer Institute for Software and Systems Engineering
Fraunhofer Institute for Software and Systems Engineering

Projects

Artificial intelligence in the edge cloud continuum (AI-ECC): Transparent, secure, and operationally usable resource and load planning

How can AI be reliably used to coordinate critical workloads in the edge-cloud continuum despite the risks? To solve this challenge, we are developing a planning model that optimizes CPU, GPU, bandwidth, and memory across the edge-cloud continuum, enables controlled resource overcommitment, and integrates these transparently into the Fraunhofer Edge Cloud (FEC). The benefits of the model are being evaluated at Fraunhofer IPT. We are also creating the technical basis with appropriate monitoring, dashboards, and automated energy data collection, as well as a clear implementation process that ensures the reliability, explainability, and policy compliance of the solution.

“Serverless EDC”: a cloud-native alternative to EDC

The Eclipse Dataspace Components Connector (EDC) is the de facto standard for connectors in data space projects. This open-source solution requires data space participants to undergo extensive training and incurs high operating costs due to regular updates. Alternative software-as-a-service (SaaS) offerings eliminate this effort, but are cost-intensive, even if the connector is rarely used, for example in demonstrators. The Serverless EDC offers a middle ground between these two operating models: it leverages the advantages of serverless architectures and relies on highly scalable cloud technologies. Data room participants retain full control over their connector and benefit from a short time-to-market. Operation, on the other hand, is billed according to the cloud operator's pay-per-use pricing model.

"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.

 

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.

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.

DiMeKI: A digitalized method for artificial intelligence (AI) supports knowledge and technology transfer in non-university research institutions

The aim of the joint project is to develop a holistic digital AI method to support knowledge and technology transfer for non-university research institutions and its prototypical application and evaluation at DFKI and Fraunhofer ISST. The transformation potential of digitalization and AI in particular will be used for knowledge and technology transfer.

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Digitalization in construction: The KLEIO project

With the ongoing digitalization, paper-based processes are a challenge in many industries, including construction. The KLEIO project aims to tackle this challenge by digitizing delivery notes for ready-mixed concrete, which are traditionally in paper form.

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The "Data Space Research Lab": Open initiative for the further development of existing Eclipse Dataspace Components from Huawei and Fraunhofer ISST

New ways for cross-border data exchange: The Data Space Research Lab is a cooperation between Huawei Technologies and Fraunhofer ISST initiated in 2023. The aim is to master the digital challenges of our time and support companies worldwide through technical innovations in the data economy.

FAIR Data Spaces – Building a joint cloud-based data space for industry and science

The FAIR Data Spaces project aims to build a common cloud-based data space for business and science by merging the two initiatives "Gaia-X" and "National Research Data Infrastructure (NFDI)". "FAIR" stands for the guiding prin-ciples for the responsible management of research data. According to these principles, research data should be findable, accessible, interoperable and reusable. The project aims to identify and exploit synergies between the two initiatives Gaia-X and NFDI and to bring the two initiatives closer together in terms of content. The project also aims to promote and demonstrate the sovereign exchange of data between industry and science at both national and European level in concrete applications and fields of work. For a wide range of actors, especially from the economy, added values can be created and demonstrated through the exchange of data.

IEDS: Incentives and Economics of Data Sharing

The aim of the project "Incentives and Economics of Data Sharing" (IEDS) is to investigate the design of incentive systems for data sharing in an entrepreneurial context through interdisciplinary collaborative research. To this end, the topic of data sharing will be analyzed from technical and economic perspectives. Specifically, insights will be gained and mechanisms developed on how to incentivize organizations to participate in open and collaborative data networks.

CCIT: Fraunhofer Cluster of Excellence Cognitive Internet Technologies

The Fraunhofer Cluster of Excellence Cognitive Internet Technologies CCIT re-searches cognitive technologies for the industrial Internet. Researchers in various disciplines are developing key technologies along the value chain, from sensors to intelligent learning methods in data processing to the cloud. CCIT helps companies with the forward-looking design of their business by opening up market access with cognitive solution services and products.

"NTT": Dataspace Connector Proof-of-Concept (PoC) / Connecting NTT with the International Data Spaces

NTT is a Japanese telecommunications company that deals with global, cross-company data exchange, among other things. A decisive factor here is data sovereignty in order to ensure that the shared data is not misused or passed on without authorization. As this is also a central topic of the International Data Spaces (IDS), the international data exchange at NTT is to be implemented as an example with the help of the IDS as part of the project. The resulting infrastructure will then continue to operate for two months.