Dynamic Resource Scaling for VNF Over Nonstationary Traffic: A Learning Approach
Bibliographic record
Abstract
Software defined networking (SDN) and network function virtualization (NFV) are key enablers for service-level customized network slicing in fifth generation (5G) core networks. Network slices are required to be isolated from each other in terms of service performance with traffic load fluctuations. In this article, the virtual network function (VNF) scalability issue is studied to meet the quality-of-service (QoS) requirement in the presence of nonstationary traffic, through joint VNF migration and resource scaling. A traffic parameter learning method based on change point detection and Gaussian process regression (GPR) is proposed, to learn traffic parameters in a fractional Brownian motion (fBm) traffic model for each stationary traffic segment within a nonstationary traffic trace. Then, the time-varying VNF resource demand is predicted from the learned traffic parameters based on an fBm resource provisioning model. With the detected change points and predicted resource demands, a VNF migration problem is formulated as a Markov decision process (MDP) with variable-length decision epochs, to maximize the long-term reward integrating load balancing, migration cost, and resource overloading penalty. A penalty-aware deep Q-learning algorithm is proposed to incorporate awareness of resource overloading penalty, with improved performance over benchmarks in terms of training loss reduction and cumulative reward maximization.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".