Emulation of the Updated CANARIE Backbone Network Topology Under IPv6 Up to 2022
Bibliographic record
Abstract
The National Research and Education Networks or Advanced Networks implemented in all countries in the world are the alternative Internet to the commercial Internet which was developed for education and experimental purposes beginning with the Internet 2. Our interest is to study the CANARIE topology after 27 years of the foundation of the pan-Canadian advanced network which now interconnects all the thirteen provinces and territories in the country. In this work a connectivity and management emulation for the updated 2022 backbone topology of CANARIE was developed under IPv6 protocols. So, emulator GNS3, Wireshark, iReasoning MIB browser, Virtual Box and Kali Linux were required to emulated CANARIE backbone infrastructure using the best possible approximation with the available resources, considering that real infrastructure is overly expensive and just supported by internet service providers. Results were successful for connectivity and management tests, showing no significative changes when Canada expanded its advanced network. Emulation uses a lot of CPU and RAM resources, but it also shows some limitations for GNS3 when comparing to real bandwidth scenarios. CANARIE maintains important connections to support international projects with other advanced networks GEANT in Europe, APAN in Asia, AFRICACONNECT through GEANT and CLARA in Latin America through Internet2 in USA. Emulating is the alternative for backbone networks for characterizing advanced networks around the world which uses expensive equipment only available by the Internet Service Provider companies.
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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".