Performance analysis of scheduling disciplines in hardware
Bibliographic record
Abstract
Due to the availability of high bandwidth resulting from high capacity links, the packets can be transmitted through the link at great speeds. Therefore, the switch has to be fast enough to be able to switch packets from several incoming links into one outgoing link at a speed that matches the available link speed. The recent developments in the ASIC design have led to the hardware realization of certain scheduling algorithms (J.C.R. Bennett et al, Proc. IEEE/ICNP, pp. 7-14, 1997). These implementations try to reduce the complexity involved in realizing the algorithms in hardware so that they can be used in high-speed networks without causing considerable delay to the packets traveling through the switch. WFQ is one of the earliest scheduling algorithms proposed to approximate GPS, which is an idealized scheduling algorithm. WF/sup 2/Q+ is an improvement over WFQ which more closely approximates GPS and is less complex to implement (when compared with WFQ). It has been shown that WF/sup 2/Q+ does not always outperform WFQ for real-time sources (P. Vellore and R. Venkatesan, IEEE Newfoundland Electrical and Computer Eng. Conf., 2002). In this paper, we show the hardware implementation of both WFQ and WF/sup 2/Q+ and estimate the differences in the complexities involved in implementing the two algorithms in hardware.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".