Planning the transformation of overlays
Bibliographic record
Abstract
Reconfiguring a topology is an important technique to sustain high efficiency and robustness of an overlay. However, the problem of transforming the overlay from an old topology to a newly refined one, at runtime, has received relatively little attention. The key challenge is to minimize the disruption that can be caused by topology transformation operations. Excessive disruption can be costly thus hamper the decision to migrate to a better topology. To address this issue, we solve a problem of finding an appropriate sequence of steps to transform a topology that incurs the least service disruption. We call this the incremental topology transformation (ITT) problem. ITT can be formulated well as an automated planning problem and can be solved with numerous off-the-shelf planning algorithms. However, we found that state-of-the-art domain-independent planning techniques can not scale to solve large ITT problem instances. This shortcoming motivated us to develop a suite of planners that use novel domain-specific heuristics to guide the search for a solution. Our empirical evaluation shows that our planners offer a viable solution to a diversity of ITT problems.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".