Orchestration and Workflows
The European Compute and Data Continuum will span multiple facilities, resource types, and governance domains. To unlock its potential, scientific workflows must be orchestrated seamlessly across HPC, cloud, edge, and specialized instruments. This requires more than ad-hoc job scheduling; rather, it calls for an integrated orchestration layer that manages resources, tasks, and data flows coherently, while ensuring reproducibility and policy compliance. For both HEP and RA, which rely on large, distributed, multi-decade collaborations, orchestration is what transforms a federation of resources into a functioning continuum.
Workflow Management
Researchers describe analyses as workflows of tasks (data read, calibration, simulation, analysis) with dependencies on data flow and order of execution. A workflow management system captures this DAG of tasks and manages execution end-to-end. These systems also provide resilience, if a job fails at one site, it is retried or moved elsewhere. Provenance metadata is recorded automatically for each step, ensuring reproducibility. For example, a HEP experiment could describe an end-to-end simulation chain from event generation through detector simulation to final analysis. The workflow engine then schedules each stage on appropriate resources (HPC, Cloud, CPU, GPU, etc.), and records all details in a database. Software portability could potentially enable the same workflow to be deployed on a HPC center, a community cloud, or an edge device at a telescope without re-engineering. Standards such as CWL (Common Workflow Language) or WDL (Workflow Description Language) provide promising baselines, but must be extended to support federated, heterogeneous execution environments. Within workflows, task scheduling ensures tasks are scheduled efficiently, respecting hardware requirements, data locality, and deadlines. For example, a GPU-intensive training job in HEP should preferentially be dispatched to accelerator-enabled nodes, while a transient detection task in RA might have to run on edge resources within milliseconds of data capture.
Resource Orchestration
Beyond describing workflows, orchestration dynamically allocates compute, storage, and network tasks across facilities. This involves mechanisms for dynamic scaling, e.g., expanding into cloud resources when HPC queues are full, and load balancing, sending tasks to underutilized sites to maximize throughput. Examples are Nextflow, Snakemake, Airflow, Galaxy, etc. For RA in particular, orchestrators must integrate with real-time data streams from telescopes, scheduling compute tasks close to the edge when latency is critical, while offloading heavier processing to HPC or cloud resources.