Multi-resource generalized processor sharing for packet processing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Middleboxes have found widespread adoption in today's networks. They perform a variety of network functions such as WAN optimization, intrusion detection, and network-level firewalls. Processing packets to serve these functions often require multiple middlebox resources, e.g., CPU and link band-width. Furthermore, different packet traffic flows may consume significantly different amounts of various resources, depending on the network functions that are applied. Multi-resource fair queueing is therefore needed to allow flows to share multiple middlebox resources in a fair manner. In this paper, we clarify the fairness requirements of a queueing scheme and present Dominant Resource Generalized Processor Sharing (DRGPS), a fluid flow-based fair queueing idealization that strictly realizes Dominant Resource Fairness (DRF) at all times. As a form of Generalized Processor Sharing (GPS) running on multiple resources, DRGPS serves as a benchmark that practical packet-by-packet fair queueing algorithm should follow. With DRGPS, techniques and insights that have been developed for traditional fair queueing can be leveraged to schedule multiple resources. As a case study, we extend Worst-case Fair Weighted Fair Queueing (WF <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Q) to the multi-resource setting and analyze its 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 | 0.001 |
| 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 it