transparent background; circles in different sizes and clours in project identity: light blue, middle and dark blue as well es lila
transparent background; visualisation of cloud, server, and the connection with devices and tasks
transparent background; edge device is visualised as tablet - conected with the cloud (3D symbol) and data symbols
transparent background; visualisation of server
transparent background; visualisation of an edge device with data

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Discover our latest updates and news about the DECICE project.
Visualisation of cloud management and real-time scheduling (data, computer, conection)
Text: "cloud management" in a blue box
Text: "real-time scheduling" in a blue box

DECICE aims to develop an AI-based, open and portable cloud management framework for automatic and adaptive optimization and deployment of applications in a federated infrastructure, including computing from the very large (e.g., HPC systems) to the very small (e.g., IoT sensors connected on the edge).

Working at such vastly different scales requires an intelligent management plane with advanced capabilities that allow it to proactively adjust workloads within the system based on their needs, such as latency, compute power and power consumption. Therefore, we envision an AI-model, which can use a digital twin of the resources available, to make real-time scheduling decisions based on telemetry data from the resources.

The DECICE framework will be able to dynamically balance different workloads, optimize the throughput and latency of the system resources (compute, storage, and network) regarding performance and energy efficiency and quickly adapt to changing conditions.

The framework also gives the necessary tools and interfaces for the administrators and deployment experts to interface with all the infrastructure components and control them to achieve the desired result. The integration of the DECICE framework with orchestration systems will be done through open standard APIs to make it portable, modular and extensible

Last but not least any planned clustering activities such as trainings, workshops and webinars will be held in close cooperation with OEHI .


LEVERAGE A COMPUTE CONTINUUM ranging from Cloud and HPC to Edge and IoT
AI-SCHEDULER supporting dynamic load balancing for energy efficient compute orchestration, improved use of Green Energy, and automated deployment.
API that increases control over network, computing and data resources.
DYNAMIC DIGITAL TWIN of the system with AI-based prediction capabilities
REAL-LIFE USE CASES of DECICE framework (usability and benefits).
SERVICE DEPLOYMENT with a high level of trustworthiness and compliance with relevant security frameworks.
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Clara Donald
Doctor of Philosophy
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