Workload characterization for the multimedia files embedded in the popular web pages
Bibliographic record
Abstract
Characterization of popular Web pages is essential to study issues such as proxy cache performance and inspection of effective resource management algorithms in Web. In this paper we used Web objects for such a characterization. A Web object is a Web page and a collection of files corresponding to the embedded objects which must be transferred to display the Web page. We collected data on the size and number of embedded objects to propose models for web objects. Due to the increase of the number of Web pages consisting of multimedia embedded objects in recent years, finding a suitable model for the Web objects including multimedia files has become an important issue. Characterization of multimedia files embedded in the Web objects is also valuable in improving their related Web page download time. In this paper, we present a characterization of top 500 popular Web sites that fall into three different data sets: February 2006, February 2005, and February 2004. We have considered the popular Web pages for this study because they are more likely to be efficiently designed, and they have significant impact on network traffic. This characterization shows the impacts of embedding multimedia files in the distribution models that we suggest for popular Web pages. This result can be used for development of workload generators that exhibit the properties of Web objects such as number of embedded objects and their sizes.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".