Digital Twin Placement for Minimum Application Request Delay With Data Age Targets
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Digital twins (DTs) are virtual implementations of physical systems (PSs) and can represent the states of the PSs in realtime. In order to update the DTs with changes in their corresponding PSs, the PSs should regularly send their state information data to the DTs. Each DT must be assigned to an execution server (ES) that processes the forwarded data from its corresponding PS. The output is then made available to applications that are operating at an Internet cloud server. In this article, we consider the problem of DT placement such that the maximum data request–response delay experienced by the application over all PSs is minimized, subject to maximum data age target constraints at the DTs and the application server. The problem is first formulated as an integer quadratic program (IQP) and then transformed into a semidefinite program (SDP). The problem is NP-complete. Since exact polynomial solutions are unavailable, several practical polynomial-time approximation algorithms are introduced. The algorithms are designed to give solutions with different tradeoffs between the accommodation of the application input timing latency and the achievement of data age targets.
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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 | 0.001 | 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 | 0.004 |
| Open science | 0.002 | 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 it