Belle II Network Performance Analysis in the Context of the WLCG Data Challenge 2024
Bibliographic record
Abstract
The Belle II experiment relies on a distributed computing infrastructure spanning 19 countries and over 50 sites. It is expected to generate approximately 40TB/day of raw data in 2027, necessitating distribution from the High Energy Accelerator Research Organization (KEK) in Japan to six data centers across the USA, Europe, and Canada. Establishing a high-quality network has been a priority since 2012 to address the challenge of transferring data across long distances in high-latency environments. This effort included joining LHCONE and conducting periodic data challenges to assess network performance after significant changes in infrastructure or experiment schedules. In February 2024 Belle II joined the WLCG Data Challenge, performed together with LHC experiments with the goal to test network performance under stress, particularly due to the anticipated increase in traffic from the experiments with the High Luminosity LHC program at CERN. In this work, we will present a comprehensive overview of the tests conducted by Belle II. We will start with the test design, define the goals, and outline the preliminary steps taken. We will then describe the working environment, the tests performed, and the tools employed. Furthermore, we will discuss the results achieved in detail. Finally, we will outline the future steps for subsequent tests.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".