An Economic Analysis of Interconnection Arrangements Between Internet Backbone Providers
Bibliographic record
Abstract
Transit and peering arrangements among Internet backbone providers (IBPs) are essential for the global delivery of communication services on the Internet. In addition, to support delay-sensitive applications (e.g., streaming and multimedia applications) it is important for IBPs to maintain high service quality even if the network is congested. One promising approach is to establish interconnection agreements among providers to dynamically trade network capacity. To make such interconnections possible in a competitive setting, we propose a pricing scheme that considers factors such as network utilization, link capacity, and the cost structure of the interconnecting participants. Our analyses show that the common sender keeps all (SKA) mode of settlement does not provide adequate incentives for collaboration; rather, the provider that delivers the packets should be suitably compensated at an equilibrium price. Two price equilibria are identified: The first favors slower IBPs, whereas the other is congestion based and can be more beneficial for faster IBPs. When cost asymmetries exist, the lower cost IBP needs to offer a price discount to induce participation. We show that a usage-based, utilization-adjusted interconnection agreement could align the costs and revenues of the providers while allowing them to meet more stringent quality of service requirements.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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 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".