Learning Auction in Coded Distributed Computing with Heterogeneous User Demands
Why this work is in the frame
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Bibliographic record
Abstract
Coded distributed computing(CDC) has shown great potentials to solve the unexpected delay caused by stragglers and communication load in distributed computing. We propose a novel learning auction to allocate computing resource efficiently in a CDC scenario. The user demand types are usually het-erogeneous according to different variation trends of the value with finish time and workload, which can be modeled by deep learning. As the goal of social welfare maximizationthe platform would allocate computing resources according to inferred value functions of users. Due to the uncertain finish time and nonlinear structures of deep learning models, the considered optimization problem is non-convex. We then reformulate the non-convex optimization problem into a mixed integer program(MIP). After analyzing the inference error caused by deep learning, a payment rule referred to VCG is designed to achieve incentive alignment and individual rationality. Besides, experiments have been performed to show the superiority of our mechanism.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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