Low complexity multi-resource fair queueing with bounded delay
Why this work is in the frame
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Bibliographic record
Abstract
Middleboxes are ubiquitous in today's networks. They perform deep packet processing such as content-based filtering and transformation, which requires multiple categories of resources (e.g., CPU, memory bandwidth, and link bandwidth). Depending on the processing requirement of traffic, packet processing for different flows may consume vastly different amounts of resources. Multi-resource fair queueing allows flows to obtain a fair share of these resources, providing service isolation across flows. However, previous solutions for multi-resource fair queueing are either expensive to implement at high speeds, or incurring high scheduling delay for flows with uneven weights. In this paper, we present a new fair queueing algorithm, called Group Multi-Resource Round Robin (GMR <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ), that schedules packets in O(1) time, while achieving near-perfect fairness with a low scheduling delay bounded by a small constant. To our knowledge, it is the first provably fair, highly efficient multi-resource fair queueing algorithm with bounded delay.
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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