Simulating Link Aggregation in Private Virtual LAN using OpenFlow for Cloud Environment
Bibliographic record
Abstract
Segregation and isolation of mission critical devices and services are among the main security concerns in cloud computing environments. Private Virtual LAN (PVLAN) offers the ability to efficiently support segregation and isolation among end devices. Link aggregation on PVLAN promiscuous ports reduces the risk of single point of failure for the entire PVLAN network. This research focuses on improving security and availability of nodes within the PVLAN domain and layer three devices by combining multiple PVLAN promiscuous ports as a single logical port using Software Defined Networking protocols. Our approach enables cloud platform to implement PVLAN by incorporating link aggregation to extend and support PVLAN features for optimal load balancing and path selection of inbound and outbound traffic. It also helps to reduce network inefficiencies which might occur from multiple traffic utilizing a single communication uplink. Simulation results show the effectiveness of our approach.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 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".