Optimal Security-Aware Virtual Machine Management for Mobile Edge Computing Over 5G Networks
Bibliographic record
Abstract
A secure execution of offloaded tasks in the 5G-driven mobile edge computing (MEC) deployment is critical for all societal sectors. To realize it, mobile network operators have to intelligently orchestrate virtual resources in multiple cloud layers to satisfy 5G security requirements. In this article, we formulate a secure virtual machine management (VMM) mechanism using the semi-Markov decision process framework that seeks to jointly minimize the service rejection and the security risk, while meeting the location awareness requirements of latency-sensitive applications in a decentralized fashion. A new metric called mean security risk is proposed to quantify the perceived risk of an offloaded application considering the number of virtual machines (VMs) that is used to execute and to protect it. We also propose a new cost structure that allows for an efficient assessment of the long-term impact of providing additional VMs to foster security services. A comparison with an optimal security-unaware VMM mechanism shows that our model provides a less risky operation at the cost of an increase in service rejection, which is caused by the use of additional VMs to shield the computation task. Finally, we show that the cost of providing security services can be significantly reduced by fine-tuning the economic gains of it.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".