Distributed FlowVisor: a distributed FlowVisor platform for quality of service aware cloud network virtualisation
Bibliographic record
Abstract
Cloud‐based virtual networking environments are required to provide fine‐grained quality of service (QoS) control without sacrificing scalability. However, no single approach can currently achieve these two goals simultaneously. FlowVisor is a building block to virtualise networks with fine‐grained QoS support; however, scalability issues caused by the OpenFlow protocol and the centralised control model are a major concern. This study introduces a distributed FlowVisor (DFVisor) platform to address these scalability issues. The proposed DFVisor uses a layered overlay mechanism to improve network addressing space and switch capacity. DFVisor uses a distributed synchronised two‐level database system with a synchronisation mechanism to enable the centralised control functions in the current FlowVisor platform in distributed control modules within the virtual network controllers. Therefore it removes a single point of failure in the network and reduces the flow setup latency without sacrificing the centralised network configuration and management capabilities. More importantly, the proposed DFVisor platform enables an advanced push‐based flow setup and statistics collection mechanism to address scalability issues caused by the current pull‐based flow setup and statistics collection method. A DFVisor prototype and an evaluation of this distributed synchronised two‐level database are presented, and key issues for future research are discussed.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".