A Prefetching Server for Reducing Startup Time of Embedded Multimedia
Bibliographic record
Abstract
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 pages including multimedia files has become an important issue for efficient catching and web page delivery time. Our characterization shows the existence of a few media objects embedded in the popular web pages that contain mostly image and text object. Also this characterization shows despite of existence of a few embedded media objects, web pages sizes are still small and the server can prefetch them efficiently in a proxy cache. Thus by prefetching the embedded objects of a web page containing a media object, we are able to reduce the delivery time for the initial portion of the multimedia object as well as the total down load time of the web page. We have developed a custom web server and a proxy cache to implement this idea. This paper shows the achieved improvement. Keywords web server, embedded objects, multimedia files, workload characterization, prefetching, network latency, heavytailed distribution.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".