Robust Virtual Network Function Optimization Under Post-Failure Uncertainty: Classical & Quantum Computing
Bibliographic record
Abstract
The Service Function Chaining (SFC) failure is a rare event that comes with escalating unexpected costs. It is an overly simplistic assumption that during the installation of the Virtual Network Function (VNF) recovery instance, the network conditions impacting the cost of redeployment will remain unchanged. In this paper, we propose a deterministic optimization model using traditional Integer Linear Program-ming (ILP) that maneuvers the resource allocation for prior and post-failure SFC deployment. Afterwards, we design a robust optimization model that accounts for the uncertainty of the redeployment costs. As per the strong duality theorem, we derive the dual formulation of the robust optimization model for reduced computational complexity. Further along this line, we propose a quantum annealing-driven quadratic optimization (QUBO) model that demonstrates inherent robustness even without ex-plicitly considering the uncertainty bounds of SFC redeployment costs. Extensive simulation studies demonstrate the superiority of robust solutions over deterministic approaches and explore the potential strengths of quantum annealing in terms of intrinsic resiliency. Although quantum computing is not yet ready to solve large-scale SFC deployment, it can support VNF services that demand ultra-low running time/real-time decision-making.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".