Bibliographic record
Abstract
As the complexity of modern networks increases, virtualization techniques, such as software-defined networking (SDN) and network function virtualization (NFV), get highlighted to achieve various network management and operating requirements. However, those virtualization techniques (specifically, NFV) have a critical issue that the performance of virtualized network functions (VNFs) is easily affected by diverse environmental factors (e.g., various workloads, resource contentions among VNFs), so resulting in unexpected performance degradations - performance uncertainty. Unfortunately, existing approaches mostly provide limited information about a single VNF or the underlying infrastructure (e.g., Xen, KVM), which is deficient in reasoning why the performance uncertainties occur. For such reasons, we first deeply investigate the behaviors of multiple VNFs along service chains in NFV environments, and define a set of critical performance features for each layer in the NFV hierarchical stack. Based on our investigations and findings, we introduce an automated analysis system, Probius, providing the comprehensive view of VNFs and their service chains on the basis of NFV architectural characteristics. Probius collects most possible NFV performance related features efficiently, analyzes the behaviors of NFV, and finally detects abnormal behaviors of NFV - possible reasons of performance uncertainties. To show the effectiveness of Probius, we have deployed 7 open-source VNFs and found 5 interesting performance issues caused by environmental factors.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.039 | 0.027 |
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".