Traffic locality characteristics in a parallel forwarding system
Bibliographic record
Abstract
Abstract Due to the widening gap between the performance of microprocessors and that of memory, using caches in a system to take advantage of locality in its workload has become a standard approach to improve overall system performance. At the same time, many performance problems finally reduce to cache performance issues. Locality in system workload is the fact that makes caching possible. In this paper, we first use the reuse distance model to characterize temporal locality in Internet traffic. We develop a model that closely matches the empirical data. We then extend the work to investigate temporal locality in the workload of multi‐processor forwarding systems by comparing locality under different packet scheduling schemes. Our simulations show that for systems with hash‐based schedulers, caching can be an effective way to improve forwarding performance. Based on flow‐level traffic characteristics, we further discuss the relationship between load‐balancing and hash‐scheduling, which yields insights into system design. Copyright © 2003 John Wiley & Sons, Ltd.
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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 | 0.002 | 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 | 0.002 | 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".