On the relevance of adversarial queueing theory in practice
Bibliographic record
Abstract
Adversarial Queueing Theory (AQT) has shown that seemingly innocent traffic injection rates might lead to unbounded queues in packet-switched networks - depending on scheduling strategies as well as topological characteristics. Little attention has been given to quantifying these effects in realistic network configurations. In particular, the existing AQT literature makes two unrealistic assumptions: infinite buffers and perfect synchrony. Because finite buffers inherently limit queue sizes, adversarial effects ultimately lead to packet loss which we address in this work. In addition, we study the effect of imperfect network synchronization under the packet loss metric. Our results, using analysis and simulation, indicate that classical AQT examples appear harmless under realistic assumptions but for a novel class of adversaries considerably higher loss can be observed. We introduce this class by giving examples of two new AQT concepts to construct loss-efficient network adversaries. Our analysis proves the robustness of these new adversaries against randomized de-synchronization effects in terms of variable link delays and nodal processing.
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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 | 0.017 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".