Using a dynamic data federation for running Belle-II simulation applications in a distributed cloud environment
Bibliographic record
Abstract
The dynamic data federation software Dynafed, developed by CERN IT, provides a federated storage cluster on demand using the HTTP protocol with WebDAV extensions. Traditional storage sites which support an experiment can be added to Dynafed without requiring any changes to the site. Dynafed also supports direct access to cloud storage such as S3 and Azure. We report on the usage of Dynafed to support Belle-II production jobs running on a distributed cloud system utilizing clouds across North America. Cloudscheduler, developed by the University of Victoria HEP Research Computing group , federates Openstack, OpenNebula, Amazon, Google, and Microsoft cloud compute resources and provides them as a unified Grid site which on average runs about 3500 Belle-II production jobs in parallel. The input data for those jobs is accessible through a single endpoint, our Dynafed instance. This Dynafed instance unifies storage resources provided by Amazon S3, Ceph, and Minio object stores as endpoints, as well as storage provided by traditional DPM and dCache sites. We report on our long term experience with this setup, the implementation of a grid-mapfile based X509 authentication/authorization for Belle-II access, and we show how a federated cluster can be used by Belle-II through gfalFS.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".