Adaptive Application Deployment for Multi-Access Edge Computing Based on Mobility Prediction
Bibliographic record
Abstract
In this paper, the proactive application deployment based on mobility prediction for multi-access edge computing (MEC) is studied. Since mobility prediction is commonly imperfect, there is an inherent conflict between the prediction accuracy and the prediction duration (i.e., the length of time ahead). As a result, proactively deploying applications for MEC based on a shorter (longer) term mobility prediction may lead to a higher (lower) accuracy, and thus reduces (increases) the service delay while suffers (avoids) from a larger deployment cost. To strike such balance, we propose a novel adaptive application deployment scheme, taking the mobility predictions of different mobile users in multiple future time periods as the input, for optimizing their corresponding application deployments (i.e., which applications should be deployed on which edge nodes and how long they should be deployed in advance). Specifically, a residual LSTM framework is utilized for mobility prediction, and based on this, a low-complexity greedy algorithm is developed. Simulation shows the feasibility of the proposed scheme and demonstrate its superiority over counterparts.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".