# DECICE: Device-Edge-Cloud Intelligent Collaboration Framework

arXiv: https://arxiv.org/abs/2305.02697 · HTML: https://mlsanity.com/paper/2305.02697/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 15 references that are in the map.
- First posted on arXiv 4 May 2023.

## Builds on (most cited first, 3 of 3)

- [Using Meta-heuristics and Machine Learning for Software Optimization of Parallel Computing Systems: A Systematic Literature Review](https://mlsanity.com/paper/1801.09444/) (2018)
- [Optimization of Heterogeneous Systems with AI Planning Heuristics and Machine Learning: A Performance and Energy Aware Approach](https://mlsanity.com/paper/2106.01441/) (2021)
- [Fine-Grained Scheduling for Containerized HPC Workloads in Kubernetes Clusters](https://mlsanity.com/paper/2211.11487/) (2022)

## Abstract (arXiv)

DECICE is a Horizon Europe project that is developing an AI-enabled open and portable management framework for automatic and adaptive optimization and deployment of applications in computing continuum encompassing from IoT sensors on the Edge to large-scale Cloud / HPC computing infrastructures. In this paper, we describe the DECICE framework and architecture. Furthermore, we highlight use-cases for framework evaluation: intelligent traffic intersection, magnetic resonance imaging, and emergency response.