A Novel Sustainable Bandwidth Allocation Strategy for Multiple Service Migration in 5G/6G Edge Computing
Bibliographic record
Abstract
Multi-containerized applications are becoming more common. Modern mobile Internet-of-Things applications, e.g., augmented reality and online gaming, have increased the need for ultra-low and real-time application responses. Edge computing is a solution for decreasing response times by relocating services closer to users. Migrating multiple services in this environment is unavoidable, given user mobility and load balancing. We utilize the MiGrror migration method, to propose a novel strategy for increasing bandwidth for short critical periods instead of maintaining increased bandwidth throughout the entire migration process. Our novel strategy drastically reduces downtime and migration time while enhancing bandwidth utilization by freeing up additional bandwidth during migration, which increases bandwidth availability for other services. The findings indicate that, even without using additional bandwidth, adjusting the timing of a bandwidth increase/decrease for services improves performance. The results are comparable to increasing the bandwidth for the entire migration duration with equivalent bandwidth consumption. Additionally, this study demonstrates that the proposed strategy is more sustainable than when using the original bandwidth, which results in less data transfer.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".