Network Information Flow in Network of Queues
Bibliographic record
Abstract
Two classic categories of models exist for computer networks: network information flow and network of queues. The network information flow model appropriately captures the multi-hop flow routing nature in general network topologies, as well as encodable and replicable properties of information flows. However, it assumes nodes in the network to be infinitely powerful and therefore does not accurately model queueing delay and loss at nodes. The network of queues model instead focuses on finite capacitied nodes and studies buffering and loss behaviors from a stochastic perspective. However, existing models on network of queues are mostly based on unrealistically simple topologies, and lacks the multi-hop flow routing dimension. In this work, we seek to combine advantages from both models. We start with the network information flow model and replace each infinitely powerful node with afinitely capacitied queue system instead. We show that the optimal routing problems for unicast, multiple unicasts and multicast can all be formulated as convex optimization problems. As a necessary step in validating the model for multicast routing, we show that network coding does not change the memoryless nature of traffic. We examine the correctness of the models through simulations and show that they behave differently than traditional link-cost based network flow models.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".