DECICE: AI-Driven Scheduling and Digital Twin Integration for the Cloud-HPC-Edge Compute Continuum
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arXiv
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| Autores principales: | , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866914598863503360 |
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| author | Sharma, Aasish Kumar Stein, Felix Aydin, Mirac Bidollahkhani, Michael Nanavati, Sachin P. Ardebili, Mohsen Seyedkazemi Mamulashvili, Giorgi Akbari, Mojtaba Decker, Jonathan Masih, Zoya Kunkel, Julian M. |
| author_facet | Sharma, Aasish Kumar Stein, Felix Aydin, Mirac Bidollahkhani, Michael Nanavati, Sachin P. Ardebili, Mohsen Seyedkazemi Mamulashvili, Giorgi Akbari, Mojtaba Decker, Jonathan Masih, Zoya Kunkel, Julian M. |
| contents | This paper presents the DECICE project (Device Edge Cloud Intelligent Collaboration framEwork), a Horizon Europe Research and Innovation Action (Grant No. 101092582, December 2022 to November 2025) that developed an open-source framework for intelligent workload scheduling across the cloud-HPC-edge compute continuum. A consortium of 12 partners across 6 European countries organized the work into six work packages covering AI-driven scheduling, digital twin infrastructure, system architecture and integration, monitoring, use case validation, and dissemination. The two core technical contributions are an Integrated AI Scheduler (IAIS) employing RNN-based prediction and formal workflow modeling for constraint-aware workload mapping, and a Digital Twin aggregating real-time metrics with carbon intensity and anomaly prediction for energy-aware scheduling. The framework operates within Kubernetes environments, supports unified workflow ingestion from multiple formats, and bridges cloud-native and HPC orchestration through a Slurm integration layer. We present the project vision, the overall architecture, contributions from each work package, quantitative evaluation results, and the open-source release. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_25292 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | DECICE: AI-Driven Scheduling and Digital Twin Integration for the Cloud-HPC-Edge Compute Continuum Sharma, Aasish Kumar Stein, Felix Aydin, Mirac Bidollahkhani, Michael Nanavati, Sachin P. Ardebili, Mohsen Seyedkazemi Mamulashvili, Giorgi Akbari, Mojtaba Decker, Jonathan Masih, Zoya Kunkel, Julian M. Distributed, Parallel, and Cluster Computing This paper presents the DECICE project (Device Edge Cloud Intelligent Collaboration framEwork), a Horizon Europe Research and Innovation Action (Grant No. 101092582, December 2022 to November 2025) that developed an open-source framework for intelligent workload scheduling across the cloud-HPC-edge compute continuum. A consortium of 12 partners across 6 European countries organized the work into six work packages covering AI-driven scheduling, digital twin infrastructure, system architecture and integration, monitoring, use case validation, and dissemination. The two core technical contributions are an Integrated AI Scheduler (IAIS) employing RNN-based prediction and formal workflow modeling for constraint-aware workload mapping, and a Digital Twin aggregating real-time metrics with carbon intensity and anomaly prediction for energy-aware scheduling. The framework operates within Kubernetes environments, supports unified workflow ingestion from multiple formats, and bridges cloud-native and HPC orchestration through a Slurm integration layer. We present the project vision, the overall architecture, contributions from each work package, quantitative evaluation results, and the open-source release. |
| title | DECICE: AI-Driven Scheduling and Digital Twin Integration for the Cloud-HPC-Edge Compute Continuum |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2605.25292 |