Dynamic scheduling of lightpaths in lambda grids
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
Dynamic optical networks hold the potential of satisfying very large bandwidth requirements of many of the grid applications. However, encapsulation of optical network elements into manageable grid resources and dynamic provisioning of lightpaths is necessary to meet the complex demand patterns of the grid applications and to optimize usage of optical network components. In this paper, we first present a scalable algorithm for an NP-hard problem of scheduling on-demand and advance reservation requests for lightpaths. We then investigate in detail the effect of proportion of advance reservations, laxity and distribution of the size of data transfer requests on performance through extensive experimentation. The paper also investigates that how much improvement in performance can be gained by segmenting large data transfer requests into multiple requests of smaller sizes and up to what percentage of overheads is segmentation justified in scheduling of lightpaths. We demonstrate how laxity can be exchanged for segmentation to achieve high utilization of lightpaths
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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