On the Fairness-Efficiency Tradeoff for Packet Processing with Multiple Resources
Bibliographic record
Abstract
Middleboxes are widely deployed in today's networks. They apply a variety of complex network functions to transform, filter, and optimize incoming traffic based on the payload of packets. These functions require the support of multiple types of resources, such as CPU and link bandwidth, for processing incoming packets. Hence, a multi-resource packet scheduling algorithm is needed to allow flows to share these resources fairly and efficiently. However, unlike traditional fair queueing where bandwidth is the only concern, we show in this paper that fairness and efficiency are conflicting objectives that cannot be achieved simultaneously in the presence of multiple resources. Ideally, a scheduling algorithm should allow network operators to flexibly specify their fairness and efficiency requirements, so as to meet the Quality of Service demands while keeping the system at a high utilization level. Yet, existing multi-resource scheduling algorithms focus on fairness only, and may lead to poor resource utilization. In this paper, we propose a new scheduling algorithm to achieve a flexible tradeoff between fairness and efficiency for packet processing, consuming both CPU and link bandwidth. Experimental results based on both real-world implementation and trace-driven simulation suggest that trading off a modest level of fairness can potentially improve the efficiency to the point where the system capacity is almost saturated.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".