Analysis of backward congestion notification with delay for enhanced ethernet networks
Bibliographic record
Abstract
Recently, companies and standards organizations are enhancing Ethernet as the unified switch fabric for all of the TCP/IP traffic, the storage traffic and the interprocess communication(IPC) traffic in Data Center Networks(DCNs). Backward Congestion Notification(BCN) is the basic mechanism for the end-to-end congestion management enhancement. To fulfill the special requirements of the unified switch fabric that being lossless and of extremely low latency, BCN should hold the queue length around a target point tightly. Thus, the stability of the control loop and the buffer size are critical to BCN. Currently, the impacts of delay on the performance of BCN are unidentified. When the link capacity increases to 40Gbps or 100Gbps in the near future, the number of on-the-fly packets becomes the same order with the shallow buffer size of switches. Thus, the impacts of delay on the performance of BCN will become significant. In this paper, we analyze BCN, paying special attention on the delay. Firstly, we model the BCN system with a set of segmented delayed differential equations. Then, the sufficient condition for the uniformly asymptotic stability of the BCN system is deduced. Subsequently, the bound of buffer occupancy under this sufficient condition are estimated, which provides guidelines on setting buffer size. Finally, the numerical analysis and the experiments on the NetFPGA platform verify the theoretical analysis.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".