Multi-Resource Round Robin: A low complexity packet scheduler with Dominant Resource Fairness
Bibliographic record
Abstract
Middleboxes are widely deployed in today's enterprise networks. They perform a wide range of important network functions, including WAN optimizations, intrusion detection systems, network and application level firewalls, etc. Depending on the processing requirement of traffic, packet processing for different traffic flows may consume vastly different amounts of hardware resources (e.g., CPU and link bandwidth). Multi-resource fair queueing allows each traffic flow to receive a fair share of multiple middlebox resources. Previous schemes for multi-resource fair queueing, however, are expensive to implement at high speeds. Specifically, the time complexity to schedule a packet is O(log n), where n is the number of backlogged flows. In this paper, we design a new multi-resource fair queueing scheme that schedules packets in a way similar to Elastic Round Robin. Our scheme requires only O(1) work to schedule a packet and is simple enough to implement in practice. We show, both analytically and experimentally, that our queueing scheme achieves nearly perfect Dominant Resource Fairness.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".