Differentiated caching of dynamic content using effective page classification
Bibliographic record
Abstract
As the use of dynamic documents increases, caching dynamic content is becoming an important issue for the usability and scalability of the Web. Dynamic content, which is not retained by current Web caching schemes, is adding significant load to Web servers and network links and hence increasing request response times. This paper proposes a scheme, called eager page dynamic caching (EPDC), to effectively cache dynamic content at proxy servers. The scheme identifies two kinds of dynamic pages, called eager-update pages and lazy-update pages, and uses different strategies to deal with each type. For eager-update pages, the Web server pushes the newest data to the proxy server after updates to the dynamic page content. For lazy-update pages, proxy servers pull the newest data from the Web server when clients request it. We use delta-encoding to decrease the amount of data transferred from the Web server to the cache server. We describe a set of simulation experiments we conducted to evaluate our scheme. We show that our scheme can achieve higher hit ratios and lower network latencies, under a variety of conditions, than both simple delta-encoding and traditional Web caching with the least recently used (LRU) scheme.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.001 | 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".