Author manuscript, published in "ACM SIGCOMM Workshop on Measurements Up the Stack, Canada (2011)" Performance of Networked Applications: The Challenges in Capturing the User’s Perception
Bibliographic record
Abstract
There is much interest recently in doing automated performance diagnosis on user laptops or desktops. One interesting aspect of performance diagnosis that has received little attention is the user perspective on performance. To conduct research on both end-host performance diagnosis and user perception of network and application performance, we designed an end-host data collection tool, called HostView. HostView not only collects network, application and machine level data, but also gathers feedback directly from users. User feedback is obtained via two mechanisms, a systemtriggered questionnaire and a user-triggered feedback form, that for example asks users to rate the performance of their network and applications. In this paper, we describe our experience with the first deployment of HostView. Using data from 40 users, we illustrate the diversity of our users, articulate the challenges in this line of research, and report on initial findings in correlating user data to system-level data.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.417 | 0.166 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".