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.
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.
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 | 0.000 | 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".