Optimal Job Scheduling and Bandwidth Augmentation in Hybrid Data Center Networks
Bibliographic record
Abstract
Optimizing data transfers is critical for improving job performance in data-parallel frameworks. In the hybrid data center with both wired and wireless links, reconfigurable wireless links can provide additional bandwidth to speed up job execution. However, it requires the scheduler and transceivers to make joint decisions under coupled constraints. In this work, we identify that the joint job scheduling and bandwidth augmentation problem is a complex mixed integer nonlinear problem, which is not solvable by existing optimization methods. To address this bottleneck, we transform it into an equivalent problem based on the coupling of its heuristic bounds, the revised data transfer representation and non-linear constraints decoupling and reformulation, such that the optimal solution can be efficiently acquired by the Branch and Bound method. Based on the proposed method, the performance of job scheduling with and without bandwidth augmentation is studied. Experiments show that the performance gain depends on multiple factors, especially the data size. Compared with existing solutions, our method can averagely reduce the job completion time by up to 10% under the setting of production scenario.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".