Design and implementation of virtual network testbeds for routing protocols
Bibliographic record
Abstract
In this project, we present the design and implementation of virtual network testbeds for studying routing changes. A virtual network testbed is a computer network that is completely created in software, while routing changes directly impact on the reliability and the reachability information of the network. We used testbeds to emulate a small and a large-scale network on a single Linux machine. These emulated networks allow the study of network behavior and operations which are examined using two routing protocols: Routing Information Protocol (RIP) and Open Shortest Path First (OSPF). We implemented a fifteen-node network to study RIP, and a model of the GÈANT network to examine OSPF in virtual network testbeds. Each testbed represents an autonomous system (AS) or an intra-domain environment. Therefore, these environments provided us with the opportunities to evaluate routing changes in an AS. We used the testbeds to compare the routing of the original network with the new routing of the missing links and routers to see what changes occur. The GÈANT network is the large-scale network used for investigations in this project. We then used our emulation results of the large-scale network to compare with the simulation work for the same network topology-the GÈANT network, and confirmed that our emulation studies also identified important links and routers in the same network. --P.ii.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".