RealNet: A Topology Generator Based on Real Internet Topology
Bibliographic record
Abstract
One of the challenges of large-scale network simulations is the lack of scalable and realistic Internet topology generators. Previous topology generators are either not scalable to millions of nodes, or not able to capture characteristics of the Internet topology. In this work, we propose a topology generator which can generate accurate large-scale models of the Internet. We extract the AS (autonomous system) level and router level topology of the Internet with various data sources such as BGP routing tables and traceroute records. With the real Internet topology, we infer the AS topology and the commercial relationship among ASes. We also group the routers into clusters according to their positions in the Internet. A compact routing core is built with the AS topology and router cluster topology. Each generated topology consists of the routing core and a set of end-hosts connected to router clusters. The generated topology is realistic since its routing core is extracted from Internet. We make the assumption of uniform routing policy within an AS. Therefore, the routing path calculation of any source/destination pair consists of finding the AS path for the source/destination ASes and finding the router level path within each AS in the AS path. Since the routing pate depends only on the routing core, its size is independent of the number of end-hosts in the generated topology.
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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 | 0.000 |
| 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".