Machine-Driven Design and Dimensioning of NFV-Enabled MEC Infrastructure to Support Heterogeneous Latency-Critical Applications
Bibliographic record
Abstract
Abstract Mobile edge computing (MEC) has recently been introduced as a key technology, emerging in response to the increased focus on new heterogeneous computing applications, resource-constrained mobile devices, and the long delay of traditional cloud data centers. Despite considerable research attention to understanding how the heterogeneous latency-critical application requirements can interact with a MEC system, there remains a dearth of literature on deploying a flexible MEC infrastructure at the mobile operator to meet the demands of an anticipated heterogeneous mobile traffic. From a dual perspective, this paper addresses the design and dimensioning of a machine-driven Network Function Virtualization-enabled MEC infrastructure problem. The proposed approach leverages a neural network model, a subset of machine learning, to predict the number of service function chains (SFCs) required for the time-varying mobile traffic load and to proactively auto-scale the different types of virtual service instances. A Mixed-Integer Linear Program (MILP) is then employed to create a physical MEC system design by mapping the predicted virtual SFC networks to the MEC nodes while minimizing deployment costs. The numerical results demonstrate that the machine learning model achieves a high prediction accuracy of 95.6%, highlighting the added value of using the ML technique at the edge network. This approach reduces deployment costs while ensuring delay requirements for different latency-critical applications and high acceptance rates.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".