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About DECICE
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 .
EDGE CLOUD DATA CENTERS KUBERNETES FRAMEWORK HETEROGENOUS SYSTEMS HPC IoT DIGITAL TWIN AI-SCHEDULING MACHINE LEARNING DEEP LEARNING SYSTEM MONITORING |
DECICE Objectives
News
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Intersection areas pose significant challenges for autonomous and conventional vehicles, particularly due to Vulnerable Road Users (VRU) being obscured by…
Security takes precedence in a computing continuum, and the DECICE project places a strong emphasis on security while building a…
Today we are going to take a look at a core component of the DECICE framework – the Virtual Training…
Marmara University, a partner and the C-ITS use case provider in the DECICE Project, recently released the first report in…
This proof-of-concept setup demonstrates the functionalities of the various sub-components and highlights potential future developments of the Digital Twin within…
The AI scheduler for storage focuses on optimizing data placement, migration, and replication across multiple storage solutions in the compute…
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- Project News
Intersection areas pose significant challenges for autonomous and conventional vehicles, particularly due to Vulnerable Road Users (VRU) being obscured by…
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A look at the DECICE main features
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Clara Donald
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