Task Dispatch through Online Training for Profit Maximization at the Cloud
Bibliographic record
Abstract
We study the scheduling of tasks that arrive dynamically at a networked cloud computing system consisting of heterogeneous processors. Execution of tasks yields some profit to the cloud service provider. We intend to maximize the total profit across all tasks arriving within a time interval, subject to processor load constraints, without prior knowledge of the task arrival times or processing requirements. We propose the Task Dispatch through Online Training (TDOT) algorithm, which consists of training and exploitation phases. We provide performance bound analysis to show that TDOT can generate profit that is close to the optimum, given a suitable size for the training task set. TDOT assumes that profit can be obtained from partially completed tasks, so we further propose a modified version of TDOT, termed TDOT-G, for implementations where profit can only be obtained from fully-completed tasks. Through simulation, using Google cluster data, we compare the performance of TDOT and TDOT-G with that of greedy scheduling, logistic regression, and an offline upper-bound solution.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".