A survey on the current internet interconnection practices
Bibliographic record
Abstract
The Internet topology has significantly changed in the past years. Today, it is richly connected and flattened. Such a change has been driven mostly by the fast growth of peering infrastructures and the expansion of Content Delivery Networks as alternatives to reduce interconnection costs and improve traffic delivery performance. While the topology evolution is perceptible, it is unclear whether or not the interconnection process has evolved or if it continues to be an ad-hoc and lengthy process. To shed light on the current practices of the Internet interconnection ecosystem and how these could impact the Internet, we surveyed more than 100 network operators and peering coordinators. We divide our results into two parts: ( i ) the current interconnection practices, including the steps of the process and the reasons to establish new interconnection agreements or to renegotiate existing ones, and the parameters discussed by network operators. In part ( ii ), we report the existing limitations and how the interconnection ecosystem can evolve in the future. We show that despite the changes in the topology, interconnecting continues to be a cumbersome process that usually takes days, weeks, or even months to complete, which is in stark contrast with the desire of most operators in reducing the interconnection setup time. We also identify that even being primary candidates to evolve the interconnection process, emerging on-demand connectivity companies are only fulfilling part of the existing gap between the current interconnection practices and the network operators' desires.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".