A formulation for IoT-enabled dynamic Service Selection across multiple Manufacturing clouds
Bibliographic record
Abstract
Cloud Manufacturing can provide mass manufacturing resources and capabilities as services via the Internet. Undoubtedly, multiple manufacturing clouds (MCs) will have extremely abundant services in terms of function, price, etc. The ability to leverage ample services hosted in MCs has direct relation to the success or failure of a manufacturer. Meanwhile, various uncertainties in today's highly-dynamic business environment can easily disrupt manufacturing activities, rendering original schedules ineffective or even obsolete. IoT's real-time sensing ability can be used to detect those uncertainties. However, little work has been done to take advantage of abundant services from MCs and to effectively deal with uncertainties. In order to address this issue, we propose a mathematical formulation for IoT-enabled dynamic Service Selection (SS) across multiple MCs. We consider three kinds of uncertainties (fluctuation of completion time, choices of manufacturing services, and runtime changes made by users) that come from both the user and market sides. The formulation can guide the dynamic SS and enable users to continuously adjust SS to be more effective and efficient.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".