Cost-Efficient Server Provisioning for Deadline-Constrained VNFs Chains: A Parallel VNF Processing Approach
Bibliographic record
Abstract
The fifth generation (5G) utilizes Network Functions Virtualization (NFV) and Software Defined Network (SDN) to provide applications. Among them, there are deadline constrained applications. When the traffic generated by applications traverses Virtual Network Functions (VNFs) chains, it may not be possible to meet the deadlines when the traffic is processed sequentially; even if very fast servers are provisioned to host the VNFs. We propose a parallel VNF processing approach for the traffic. This is done through pools of candidate servers that host VNFs which process the traffic. Considering the routing policy provided by the SDN routing application, the server selection problem with the aim of deadline satisfaction and cost minimization is modeled as a non-linear binary optimization problem. An iterative search algorithm is proposed to solve this problem. Simulations show enhancement in deadline satisfaction as well as cost reduction.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".