Architecture design, performance analysis and VLSI implementation of a reconfigurable shared buffer for high‐speed switch/router
Bibliographic record
Abstract
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 memory technology perspective, while DRAM is a good choice to meet capacity requirement, the access time causes problems for high‐speed applications. On the other hand, though SRAM is faster, it is more costly and does not have high storage density. The SRAM/DRAM hybrid architecture provides a good solution to meet both capacity and speed requirements. From the switch design and network traffic perspective, to minimize packet loss, the buffering space allocated for each switch port is normally based on the worst‐case scenario, which is usually huge. However, under normal traffic load conditions, the buffer utilization for such configuration is very low. Therefore, we propose a reconfigurable buffer‐sharing scheme that can dynamically adjust the buffering space for each port according to the traffic patterns and buffer saturation status. The target is to achieve high performance and 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 by both a numerical model and extensive simulations under uniform and non‐uniform traffic conditions. We also present the architecture design and VLSI implementation of the proposed reconfigurable shared buffer using the 0.18 µmCMOStechnology. Our results manifest that the proposed architecture can always achieve high performance and provide much flexibility for the high‐speed packet switches to adapt to various traffic patterns. Furthermore, it can be easily integrated into the functionality of port controllers of modern switches and routers. Copyright © 2008 John Wiley & Sons, Ltd.
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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 | 0.000 |
| 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) | 0.002 | 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".