Performance Modeling of a Reconfigurable Shared Buffer for High-Speed Switch/Router
Bibliographic record
Abstract
Modern switches and routers require massive storage space to buffer packets. This becomes more significant as link speed increases and switch size grows. From the switch design and network traffic perspective, to minimize packet loss, the buffering resource allocated for each switch port is normally based on the worst case scenario, which is usually huge. However, under normal load conditions, the buffer utilization for such configuration is very low. Therefore, we propose a reconfigurable buffer sharing scheme, which is based on the hybrid SRAM/DRAM architecture and can flexibly adjust the buffering space for each port according to the traffic pattern and buffer saturation status. The target is to improve buffer utilization, while not posing much constraint on the buffer speed. In this paper, we study the performance of the proposed buffer sharing scheme using an iterative analytical model under uniform traffic. Performance under non-uniform traffic is studied through simulations. The simulation results fit well with those from the analytical model. Moreover, our results demonstrate that significant performance enhancement can be achieved by sharing the port buffering resources.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".