RAP-G: Reliability-aware service placement using genetic algorithm for deep edge computing
Bibliographic record
Abstract
To ensure low latency, service providers are increasingly turning to edge computing, pushing services and resources from the Cloud to the Edge of the network, as close as possible to users. However, since video and image processing applications are particularly computationally intensive, their deployment is typically based on distributed provisioning between the Edge and the Cloud, which can increase the risk of failure when relying on unreliable networks. In this work, we proposed the algorithm RAP-G (Reliability-Aware service Placement with Genetics), which considers the reliability of network links and distributes services between the Cloud and the Edge using a genetic algorithm (GA). We have also developed a new variant of the first-fit algorithm called RF2 (Reliability-Aware First-Fit) that considers reliability within a reasonable time. The performance of the RAP-G algorithm was evaluated and compared with the RF2 algorithm. The experimental results show the importance of considering reliability in service delivery and the superiority of RAP-G.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".