Admission and Placement Policies for Latency-Compliant Secure Services in 5G Edge–Cloud System
Bibliographic record
Abstract
This article proposes an optimal admission and placement stochastic controller that inserts security and latency compliance in the operational aspects of edge–cloud system under a fifth generation (5G) deployment. The proposed mechanism uses the framework of semi-Markov decision making process and seeks for an optimal policy that efficiently allocates the virtual resources to secure and run the services across the cloudified infrastructure. Driven by a new latency-oriented cost structure, the optimal controller achieves a secure and latency compliant operation by optimally balancing the service requests between the edge and the cloud system taking into account the service profile, the workload, and the traffic load. A structural analysis of the optimal policy reveals its implementation friendliness, which is key for its deployment or derivation of suboptimal mechanisms. Numerical results unveil that the admission and placement decision making process does not adversely impact the performance of the admission decision making process. Finally, a cloudnomics analysis shows that the optimal cost can be further optimized by fine tuning the parameters of the proposed cost structure. In this respect, numerical results show a reduction of approximately 162% for some cases of the scenario under analysis.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".