Competitive buffer management for shared-memory switches
Why this work is in the frame
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Bibliographic record
Abstract
We consider buffer management policies for shared memory switches. We study the case of overloads resulting in packet loss, where the constraint is the limited shared memory capacity. The goal of the buffer management policy is that of maximizing the number of packets transmitted. The problem is online in nature, and thus we use competitive analysis to measure the performance of the buffer management policies. Our main result is to show that the well-known preemptive Longest Queue Drop ( LQD ) policy is at most 2-competitive and at least √2-competitive. We also demonstrate a general lower bound of 4/3 on the performance of any deterministic online policy. Finally, we consider some other popular non-preemptive policies including Complete Partition, Complete Sharing, Static Threshold and Dynamic Threshold and derive almost tight bounds on their performance.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it