Peering the Edge: Enabling Low-Latency Interdomain Edge Communication via Collaborative Transmission
Bibliographic record
Abstract
Enabling low-latency end-to-end interdomain communication is critical in edge networks. However, the current network architecture results in unnecessarily long communication paths, leading to high latency between devices. To address this issue, we propose a novel interdomain edge peering framework called Collie. In Collie, edge networks belonging to different network providers collaborate to forward traffic towards destinations, effectively reducing end-to-end communication latency. Importantly, Collie allows network providers to maintain their autonomy in link usage strategy. We also develop a distributed algorithm in Collie that enables edge nodes from different networks to collectively determine optimal routing and traffic assignment, ensuring low-latency delivery while respecting network policies without exposing them. We implement a prototype of Collie and extensively evaluate its performance using real-world topologies. Our results demonstrate that Collie achieves a tight approximation ratio and exhibits scalability in large interdomain edge networks.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".