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.

Data-driven models can predict filesystem I/O time proportion, a key metric for guiding I/O tuning in HPC applications, from performance data. This work evaluates whether the XGBoost-based model we proposed in our prior work [13], remains effective when the underlying profiling tool changes. Using I/O metrics collected with Score-P and DFTracer, we analyze two video processing workloads and train predictive models. Results show that predictive accuracy remains stable across profiling tools while revealing workload-specific differences in I/O behavior, suggesting that the relationship between configuration parameters and the proportion of filesystem I/O time is preserved despite variations in measurement methodology. Furthermore, the findings indicate that configuration-driven models can approximate I/O time proportion without requiring runtime tracing for every execution, reducing dependence on instrumentation in future machine learning-driven performance analysis workflows.

The decision to optimize I/O in scientific applications often depends on the proportion of I/O time within an application's runtime. For AI workloads, such as machine learning (ML), we observe that different configurations with the same data size can lead to varying I/O impacts. In this work, we analyze common tunable parameters—batch size, number of samples, and number of files—typically adjusted when adapting ML models to new datasets or use cases. Using the XGBoost model, we predict the I/O percentage for ResNet50 and UNet3D workloads and identify feature combinations that influence I/O behavior. Our results provide practical guidance for optimizing I/O performance in AI workloads.