DECICE
Device-Edge-Cloud Intelligent Collaboration framEwork
Full project details (EU Research results portal): https://cordis.europa.eu/project/id/101092582
Project description:
The cloud computing industry has grown massively over the last decade and with that new areas of application have arisen. Some areas require specialized hardware, which needs to be placed in locations close to the user. User requirements such as ultra-low latency, security and location awareness are becoming more and more common, for example, in Smart Cities, industrial automation and data analytics. Modern cloud applications have also become more complex as they usually run on a distributed computer system, split up into components that must run with high availability. Unifying such diverse systems into centrally controlled compute clusters and providing sophisticated scheduling decisions across them are two major challenges in this field. Scheduling decisions for a cluster consisting of cloud and edge nodes must consider unique characteristics such as variability in node and network capacity. The common solution for orchestrating large clusters is Kubernetes, however, it is designed for reliable homogeneous clusters. Many applications and extensions are available for Kubernetes. Unfortunately, none of them accounts for optimization of both performance and energy or addresses data and job locality.In DECICE, we develop an open and portable cloud management framework for automatic and adaptive optimization of applications by mapping jobs to the most suitable resources in a heterogeneous system landscape. By utilizing holistic monitoring, we construct a digital twin of the system that reflects on the original system. An AI-scheduler makes decisions on placement of job and data as well as conducting job rescheduling to adjust to system changes. A virtual training environment is provided that generates test data for training of ML-models and the exploration of what-if scenarios. The portable framework is integrated into the Kubernetes ecosystem and validated using relevant use cases on real-world heterogeneous systems.
EuroVoc IDs: /social sciences/sociology/industrial relations/automation
EU Programme: Horizon Europe
EU Project
Project publications:
| EU Project | Has Title | Has Category | Has Type | Has Year | Has DOI |
|---|---|---|---|---|---|
| DECICE | PM100: A Job Power Consumption Dataset of a Large-scale Production HPC System | Hardware Architecture and High-Performance Computing (HPC) | Conference proceedings | 2025 | https://doi.org/10.1145/3624062.3624263 |
| DECICE | DECICE | Hardware Architecture and High-Performance Computing (HPC) | Conference proceedings | 2023 | https://doi.org/10.48550/arxiv.2305.02697 |
| DECICE | TinyLid: a RISC-V accelerated Neural Network For LiDAR Contaminant Classification in Autonomous Vehicle | Hardware Architecture and High-Performance Computing (HPC) | Conference proceedings | 2025 | https://doi.org/10.1145/3649153.3649201 |
| DECICE | Directly-trained Spiking Neural Networks for Deep Reinforcement Learning: Energy efficient implementation of event-based obstacle avoidance on a neuromorphic accelerator | Hardware Architecture and High-Performance Computing (HPC) | Peer reviewed articles | 2025 | https://doi.org/10.1016/J.NEUCOM.2023.126885 |
| DECICE | Graph Neural Networks for Anomaly Anticipation in HPC Systems | Hardware Architecture and High-Performance Computing (HPC) | Conference proceedings | 2025 | https://doi.org/10.1145/3578245.3585335 |
| DECICE | Towards Nano-Drones Agile Flight Using Deep Reinforcement Learning | Hardware Architecture and High-Performance Computing (HPC) | Conference proceedings | 2024 | https://doi.org/10.1109/COINS61597.2024.10622558 |
| DECICE | SpikeStream: Accelerating Spiking Neural Network Inference on RISC-V Clusters with Sparse Computation Extensions | Hardware Architecture and High-Performance Computing (HPC) | Conference proceedings | 2025 | https://doi.org/10.23919/DATE64628.2025.10992749 |
| DECICE | Performance Characterization of Hardware/Software Communication Interfaces in End-to-End Power Management Solutions of High-Performance Computing Processors | Hardware Architecture and High-Performance Computing (HPC) | Peer reviewed articles | 2025 | https://doi.org/10.3390/EN17225778 |
| DECICE | Bio-Inspired Drone Control: A Reinforcement Learning-Trained Spiking Neural Networks for Agile Navigation in Dynamic Environment | Hardware Architecture and High-Performance Computing (HPC) | Conference proceedings | 2025 | https://doi.org/10.1109/COINS65080.2025.11125776 |
| DECICE | M100 ExaData: a data collection campaign on the CINECA s Marconi100 Tier-0 supercomputer | Hardware Architecture and High-Performance Computing (HPC) | Peer reviewed articles | 2023 | https://doi.org/10.3929/ethz-b-000614369 |
| DECICE | Fair and efficient resource allocation via vehicle-edge cooperation in 5G-V2X networks | Hardware Architecture and High-Performance Computing (HPC) | Peer reviewed articles | 2024 | https://doi.org/10.1016/J.VEHCOM.2024.100773 |
| DECICE | GRAAFE: GRaph Anomaly Anticipation Framework for Exascale HPC systems | Hardware Architecture and High-Performance Computing (HPC) | Peer reviewed articles | 2024 | https://doi.org/10.1016/J.FUTURE.2024.06.032 |
| DECICE | The REGALE Library: A DDS Interoperability Layer for the HPC PowerStack | Hardware Architecture and High-Performance Computing (HPC) | Peer reviewed articles | 2025 | https://doi.org/10.3390/JLPEA15010010 |