Towards Network-Aware Divisible Load Theory for Optical Grids
Bibliographic record
Abstract
Most grid applications require the processing of large amounts of data stored at different locations across the network which makes optical grid infrastructures optimal for such applications. The increase in intensity of data- and communications of these applications calls for new mechanisms and theories on how to optimally allocate optical grid resources. Typical Divisible Load Theory (DLT) makes optimal allocation of computational resources. In this paper, we introduce Network Aware Divisible Load Algorithm (NADLA) that extends DLT to optimally allocate both the computational and networking resources of an optical grid. We assume a data- and communications-intensive application where data is stored at different sites across the optical grid and can be divided into independent subsets to be processed in parallel at different sites. The algorithm defines an optimal data transfer schedule that defines when to transfer what data subsets to which sites across the optical grid in order to minimize the overall application completion time. It consists of two phases. In the first phase, NADLA provide load distribution that minimizes application completion time taking into account network connectivity and computational and networking resources availability. Site connectivity is estimated by considering the bandwidth and the expected free time of all the links connecting this site to other sites. In the second phase, a simple greedy algorithm is used to allocate computational and networking resources. Extensive Simulations are conducted to examine the performance of the proposed algorithm for different application types and sizes, and different optical grid topologies. Simulation results show the advantages of the proposed algorithm over the traditional DLT approach for the category of applications considered.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".