Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it