NOTED: An intelligent network controller to improve the throughput of large data transfers in File Transfer Services by handling dynamic circuits
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The NOTED (Network Optimised Transfer of Experimental Data) project has successfully demonstrated the ability to dynamically provision network links to increase the effective bandwidth available for FTS-driven transfers between endpoints, such as WLCG sites, by inspecting on-going data transfers and so identifying those that are bandwidth-limited for a long period of time. Recently, the architecture of NOTED has been improved and the software has been packaged for easy distribution. These improved capabilities and features of NOTED have been tested and demonstrated at various international conferences. For example, during demonstrations at Supercomputing 2022, independent instances of NOTED at CHCERN (Switzerland) and DE-KIT (Germany) monitored large data transfers generated by the ATLAS experiment between these sites and CA-TRIUMF (Canada). We report here on this and other events, highlighting how NOTED can predict link congestion or a notable increase in the network utilisation over an extended period of time and, where appropriate, automatically reconfigure network topology to introduce an additional or an alternative and betterperforming path by using dynamic circuit provisioning systems such as SENSE and AutoGOLE.
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.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.000 |
| 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 it