An Implementation of Cloud-based Grid CE and SE For ATLAS and Belle II
Bibliographic record
Abstract
The economies of scale realised by institutional and commercial cloud providers make such resources increasingly attractive for grid computing. We describe an implementation of this approach which has been deployed for Australia’s ATLAS and Belle II grid sites. The sites are built entirely with virtual machines (VM) orchestrated by an Open- Stack instance. The storage element (SE) utilises an XRootD-S3 gateway with back-end storage provided through an S3-compatible object store from a commercial provider. The provisioning arrangements required the deployment of some site-specific helper modules to ensure all SE interfacing requirements could be met. OpenStack hosts the XRootD redirector and proxy servers in separate VMs. The compute element (CE) comprises virtual machines (VM) within the Open-Stack instance. Jobs are submitted and managed by HTCondor. A CloudScheduler instance is used to coordinate the number of active OpenStack VMs and ensure that VMs run only when there are jobs to run. Automated configuration of the individual VMs associated with the grid sites is managed using Ansible. This approach was chosen due to its low overheads and the simplicity of deployment. Performance metrics of the resulting grid sites are presented to illustrate the viability of this cost-effective approach to resource provisioning for grid computing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".