MAGHE: A Predictive Mobility-Aware Framework for VNF Embedding in B5G Networks
Bibliographic record
Abstract
In Beyond Fifth Generation (B5G) networks, support for low-latency, high-mobility services such as augmented reality and autonomous systems demands efficient and predictive placement of Virtual Network Functions (VNFs). Traditional embedding algorithms like the Greedy Based Algorithm (GBA) and the Greedy Cost-Based Algorithm (GCBA) are inherently reactive, relying on current user positions and failing to adapt to user mobility patterns. We introduce MAGHE, a Mobility-Aware Greedy Heuristic Embedding framework that augments these baseline algorithms through predictive intelligence. MAGHE integrates machine learning-based trajectory forecasting using XGBoost and geospatial clustering to proactively embed VNFs near users’ anticipated locations. This design enables latencyaware, proactive placement across mobile edge infrastructure, an essential capability for B5G service continuity. Evaluations using real-world urban mobility traces demonstrate that MAGHE reduces average user-to-VNF latency by 3.6% compared to reactive baselines, while maintaining a 100% embedding success rate. These results showcase MAGHE’s potential as a lightweight, practical solution for mobility-aware VNF management in future B5G networks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".