On Fairness-Efficiency Tradeoffs for Multi-resource Packet Processing
Bibliographic record
Abstract
Middleboxes are widely deployed in today's datacenter networks. They perform a variety of network functions, each requiring multiple hardware resources, such as CPU cycles and link bandwidth. Depending on the functions they go through, packet processing of different traffic flows may consume a vastly different amount of hardware resources. An effective algorithm is therefore highly desired to schedule packets in a way such that multiple resources are shared in a fair and efficient manner. However, we show in this paper that there exists a fairnessefficiency tradeoff when multiple resources are scheduled. Such a tradeoff has never been a problem for traditional singleresource fair queueing (e.g., GPS, WFQ, SCFQ, DRR) - as long as the queueing schemes are work conserving, both fairness and efficiency can be achieved simultaneously - and hence has received little attention. Therefore, a new and important research problem arises: given a desired fairness-efficiency tradeoff, how can we design a packet scheduling algorithm to reinforce such a tradeoff? We present our thoughts and observations in this paper.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 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".