A dynamic scheduling benchmark: design, implementation and performance evaluation
Bibliographic record
Abstract
Puzzles have traditionally been used as popular benchmarks for evaluating different problem solving strategies. Many of the game benchmarks are suitable for evaluating static scheduling techniques. In such benchmarks, the scheduling phase and the execution phase (i.e. when the schedule is executed to play the game) are disjoint. The scheduling technique can be executed to compute a complete schedule prior to the execution of any moves to play the game. Due to recent interest in on-line problem solving techniques, there is a need for benchmarks which can evaluate the performance trade-offs of dynamic scheduling techniques. Many modern video and computer games can be suitable candidates for dynamic scheduling benchmarks, since they require on-line problem solving. These benchmarks and their system testbeds should be chosen and implemented such that they can accurately reveal important performance trade-offs of dynamic scheduling techniques. In this paper, we introduce a dynamic scheduling benchmark and its system testbed. This benchmark is based on an extended version of the Tetris computer game. The rules and semantics of the game were modified to lend themselves well to evaluation of discrete problem solving and optimization techniques. The system testbed is implemented in a distributed and asynchronous fashion, on a network of workstations, to reveal performance trade-offs between scheduling time, schedule quality, and problem constraints.
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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 | 0.001 | 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 | 0.001 |
| 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".