DFVisor: Scalable network virtualization for QoS management in cloud computing
Bibliographic record
Abstract
Increasingly cloud-based virtual networking environments are required to provide fine-grained Quality of Service (QoS) management without sacrificing scalability. However, no single approach currently can meet these requirements simultaneously. This paper introduces a layered concept that uses a common overlay mechanism to virtualize networks and enable fine-grained QoS management through resource slicing. Based on this mechanism, a fully distributed network virtualization platform called Distributed FlowVisor (DFVisor) is proposed. It uses a layered overlay to improve the network addressing space, reduce flow setup latency, and remove the single point of failure in the network - the central slice controller. Through a distributed synchronized two-level database, DFVisor incorporates a push-based flow setup and statistics collecting mechanism using a dedicated data channel to address the scalability issues caused by the current pull-based mechanism and the limited control channel bandwidth shared by both control flows and network statistics. The potential issues in DFVisor implementation and evaluation are discussed for future research.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".