Exploiting Virtualization and Cloud Computing in ATLAS
Bibliographic record
Abstract
The ATLAS Computing Model was designed around the concept of grid computing; since the start of data-taking, this model has proven very successful in the federated operation of more than one hundred Worldwide LHC Computing Grid (WLCG) sites for offline data distribution, storage, processing and analysis. However, new paradigms in computing, namely virtualization and cloud computing, present improved strategies for managing and provisioning IT resources that could allow ATLAS to more flexibly adapt and scale its storage and processing workloads on varied underlying resources. In particular, ATLAS is developing a "grid-of-clouds" infrastructure in order to utilize WLCG sites that make resources available via a cloud API. This work will present the current status of the Virtualization and Cloud Computing R&D project in ATLAS Distributed Computing. First, strategies for deploying PanDA queues on cloud sites will be discussed, including the introduction of a "cloud factory" for managing cloud VM instances. Next, performance results when running on virtualized/cloud resources at CERN LxCloud, StratusLab, and elsewhere will be presented. Finally, we will present the ATLAS strategies for exploiting cloud-based storage, including remote XROOTD access to input data, management of EC2-based files, and the deployment of cloud-resident LCG storage elements.
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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 | 0.001 | 0.001 |
| 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 | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".