Latency Amplification: Characterizing the Impact of Web Page Content on Load Times
Bibliographic record
Abstract
Web users like sites that load quickly. Longer web page load times translate to reduced user satisfaction and loss of revenue and mindshare. The time required to load a given web page is difficult to predict because it is a complex function of many factors, such as the latencies associated with the network requests used to retrieve that content from remote servers. However, one of the most important factors is the page content, including the scripts, images, style sheets and other objects that are present on the page. In this paper we propose a simple metric for characterizing the content of a web page in terms of its impact on page loading times. This metric, called the latency amplification factor (LAF), characterizes the content of a web page in terms of how it affects the page load time. The LAF of a web page can be estimated quickly and easily, and we describe a lightweight method for doing so. In addition, we propose an extended version of the basic LAF metric, called CLAF, that relates page load time to underlying request latencies in the presence of content delivery networks. We estimated LAFs for a variety of popular web sites, and found that they varied substantially. To validate our approach for estimating LAFs, we compared estimated LAFs against measured LAFs and found that our methodology, though simple, gave reasonably accurate estimates.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".