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 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".