A New Comprehensive Mathematical Model for Heterogeneous Multi-service Hybrid Migration in Edge Computing
Why this work is in the frame
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Bibliographic record
Abstract
Integrating cloud, fog, and edge computing with future Internet-of-Things (IoT) devices and their applications in 5G/6G networks will become more feasible soon. This research analyzes the effectiveness of the Hybrid-MiGrror and hybrid-copy migration methods in scenarios involving heterogeneous multiple VMs/containers. This research introduces mathematical models for the Hybrid-MiGrror approach and offers suggestions and comparisons for migrating services to be deployed as multiple services. The model’s notable feature is its ability to use both average and non-average values for various parameters during migration, resulting in enhanced and more precise outcomes. In contrast, previous hybrid migration studies mostly rely only on average values. This study demonstrates that relying solely on average parameter values in hybrid migration can result in imprecise outcomes.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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