Bibliographic record
Abstract
This article studies the "filter effects" that occur in Web proxy caching hierarchies due to the presence of multiple levels of caches. That is, the presence of one level of cache changes the structural characteristics of the workload presented to the next level of cache, since only the requests that miss in one cache are forwarded to the next cache.Trace-driven simulations, with empirical and synthetic traces, are used to demonstrate the presence and magnitude of the filter effects in a multilevel Web proxy caching hierarchy. Experiments focus on the effects of cache size, cache replacement policy, Zipf slope, and the depth of the Web proxy caching hierarchy.Finally, the article considers novel cache management techniques that can better exploit the changing workload characteristics across a multilevel Web proxy caching hierarchy. Trace-driven simulations are used to evaluate the performance of these approaches. The simulation results demonstrate that size-based partitioning and heterogeneous cache replacement policies each offer improvements in overall caching performance. The sensitivity of the results to the degree of workload overlap among child-level proxy caches is also studied.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".