Synthetic trace generation for the Internet
Bibliographic record
Abstract
1 Introduction It is well established that memory reference strings of computer programs exhibit spatial and temporal locality [1]. This locality is the motivating concept behind the use of instruction and data caches, which play a critical role in improving the performance of contemporary computer systems. In this paper, we apply the concepts of locality and workload modeling originally developed for investigating program memory references to the characterization of traces of Internet traffic. Internet routers use locally-stored routing tables to look up the correct outgoing interface for each incoming IP packet, based on the best match for the destination address extracted from its header. In this sense, the destination address is the index to the routing table, just as a virtual memory address is an index to the page directories and page tables. The sequence of destination addresses in the IP packet trace constituting the input to this lookup process exhibits both temporal and spatial locality. This similarity has been exploited, e.g., in [2], to accelerate routing table lookups by harnessing for this purpose the caching hardware used by virtual address translation.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".