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.

Cloud computing clusters, especially those handling cloud workloads, require efficient job scheduling to optimize resource utilization and minimize completion time. Traditional approaches often fall short in dynamic cloud environments. We propose “HOSHMAND” (High-performance Open sourced AI-based Scheduling Handler for MAnaging Node Distribution), an AI-driven framework using a custom-tailored Recurrent Neural Network (RNN) to rapidly predict the most suitable nodes for cloud workload execution. A key feature of HOSHMAND is its accelerated scheduling capability, which significantly reduces the time required for job allocation compared to traditional methods. This is particularly crucial for cloud environments with fluctuating workloads and diverse computational requirements. A distinct capability of HOSHMAND is its proficiency in managing heterogeneous resources, ensuring optimal allocation regardless of varying computational capabilities or resource types. This adaptability is crucial for contemporary cloud computing en-vironments, which often comprise a diverse array of hardware configurations, to maintain high efficiency and resource utilization. Moreover, HOSHMAND mitigates the overhead associated with repetitive scheduling computations in similar scenarios by leveraging its historical knowledge. Upon recognizing a con-figuration of jobs analogous to previously encountered situations, it promptly enacts the most effective scheduling strategy without redundant recalculations. This predictive capability not only conserves computational resources but also accelerates job execution. Our approach, tested on cloud-based datasets, demonstrates remarkable improvements in scheduling speed and efficiency, validated by reduced time-to-schedule and enhanced overall system throughput. Through its innovative handling of heterogeneous resources and intelligent avoidance of unnecessary scheduling computations, HOSHMAND sets a new benchmark for AI -driven job scheduling in cloud computing environments.