Service Function Chaining in MEC: A Mean-Field Game and Reinforcement Learning Approach
Why this work is in the frame
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Bibliographic record
Abstract
Multiaccess edge computing (MEC) and network virtualization technologies are important enablers for fifth generation networks to deliver diverse services. Services are often provided as fully connected virtual network functions (VNFs), through service function chaining (SFC). However, the problem of allocating SFC resources at the edge still faces many challenges related to the way VNFs are placed, chained, and scheduled. In this article, to solve these problems, we propose a game theory-based approach with the objective to reduce service latency in the context of SFC at the edge. The problem of allocating SFC resources can be divided into two subproblems: 1) the VNF placement and routing subproblem, and 2) the VNF scheduling subproblem. For the former subproblem, we formulate it as a mean-field game in which VNFs are contending over edge resources aiming at reducing the resource consumption of MEC nodes and reducing latency for users. We also propose a reinforcement learning-based technique, where the Ishikawa--Mann learning algorithm is used. For the later subproblem, we formulate it as a matching game between VFNs and edge resources to find the execution order of the VNFs while reducing the latency. To efficiently solve it, we propose a modified version of the many-to-one deferred acceptance algorithm (DAA), called the enhanced multistep DAA. To illustrate the performance of the proposed approaches, we perform extensive simulations. The results show that the approaches achieve up to 40% less resource consumption, and up to 38% less latency than the benchmarked state-of-the-art methods.
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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 | 0.002 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it