DECICE: AI-Driven Scheduling and Digital Twin Integration for the Cloud-HPC-Edge Compute Continuum

2026-05-24

AI Summary

This work addresses the challenges of intelligent workload scheduling and resource coordination across the cloud–HPC–edge computing continuum by proposing an open-source, AI-driven unified scheduling framework. The framework integrates an Integrated AI Scheduler (IAIS) with digital twin technology: IAIS leverages RNN-based time-series forecasting and formal workflow modeling to enable constraint- and energy-aware scheduling, while the digital twin incorporates real-time performance metrics, carbon intensity data, and anomaly predictions to support dynamic decision-making. Designed for interoperability, the system supports both Kubernetes and Slurm, bridging cloud-native and HPC ecosystems and accommodating diverse workflow formats. Validated across twelve institutions in six countries under varied scenarios, the framework demonstrates significant improvements in scheduling efficiency and energy effectiveness and has been publicly released as open-source software.

Abstract

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.

Problem

• cloud-HPC-edge continuum
• intelligent workload scheduling
• energy-aware scheduling
• constraint-aware mapping
• compute continuum

Innovation

• AI-driven scheduling
• Digital Twin
• Cloud-HPC-Edge continuum
• Energy-aware scheduling
• Workflow orchestration