Agile Design and Dimensioning of MEC-NFV Infrastructure to Support Heterogeneous Service Chains
Bibliographic record
Abstract
Mobile edge computing (MEC) has recently become a key technology that appeared in response to the increasing focus on the emergence of new heterogeneous service chain requests, the resource-constrained mobile devices, and the long delay provided by the conventional Cloud Data Centers. Although many researchers have investigated how the heterogeneous service function chain (SFC) requests can interact with the MEC system, very few have tackled how to deploy a flexible MEC infrastructure at the mobile operator for the expected mobile traffic. This paper addresses the design and dimensioning of an agile MEC infrastructure problem with aid of Network Function Virtualization (NFV) technology. To improve physical resource utilization, we consider virtual instances resources overheads and virtual instances shareability in the deployment process. A mixed-Integer Linear Program (MILP) is used to construct a physical MEC system design by mapping the received SFCs' virtual networks to the dimensioned MEC nodes while minimizing the deployment cost. Numerical results highlight the value gained in reducing the deployment cost while guaranteeing the delay requirements for different SFCs with high acceptance rates.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".