Cost-Efficient Learn-and-Adapt Online Service Function Chain Deployment in Edge Networks
Bibliographic record
Abstract
The integration of network function virtualization (NFV) with mobile edge computing (MEC) fosters a more agile service provisioning in a network operational cost-efficient manner. However, some challenges exist in adapting to the unpredictable network stochastics and resource restrictiveness, when placing virtualized network functions (VNFs) or service function chains (SFSs) appropriately onto MEC networks. In this work, we study the cost-efficient online SFC deployment in MEC networks, where each service is translated as an SFC flow and traverses through networks to meet service demands. First, we formulate a long-term time-averaged network operational cost minimization problem, by optimizing both SFC mapping and flow routing, to keep the system stability. Then, to deal with the non-trivial mixed-integer programming (MIP) and stochasticity properties in the SFC deployment, we use both L<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</inf>(0 <p< 1) norm-based relaxation and penalization, and learn-and-adapt techniques, to obtain an improved performance-stability tradeoff. Finally, both theoretical analyses and numerical simulations are conducted to demonstrate the proposed method’s superiority, in terms of its asymptotic optimality and reduced queue backlog.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".