AttentiveDRL: Fair and Efficient GPU Job Scheduling via Dual-Agent RL
Bibliographic record
Abstract
Public cloud GPU clusters are increasingly used for distributed deep learning, making job scheduling critical for minimizing waiting and completion times. However, this scheduling problem is NP-hard, and current approaches typically separate job scheduling from GPU allocation, yielding suboptimal performance. While deep reinforcement learning (DRL)-based methods offer flexibility, they often overlook two key challenges: (1) focusing solely on total completion time while ignoring fairness in user waiting times, and (2) neglecting GPU communication costs that significantly impact distributed training speed. We introduce AttentiveSched, a DRL-based framework that simultaneously optimizes job selection and GPU assignment. AttentiveSched leverages cluster topology for informed scheduling decisions, employing attention-based agents to capture global relationships in input sequences. Through reward functions addressing fairness, completion time, and communication costs, AttentiveSched out-performs multiple heuristics-based, meta-heuristics-based, and DRL-based schedulers on real-world datasets.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".