From network-level measurements to expected quality of experience: The Skype use case
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Modern Internet applications rely on rich multimedia contents making the quality of experience (QoE) of end users sensitive to network conditions. Several models were developed in the literature to express QoE as a function of measurements carried out on the traffic of the applications themselves. In this paper, we propose a new methodology based on machine learning able to link expected QoE to network and device level measurements outside the applications' traffic. This direct linking to network and device level measurements is important for the prediction of QoE. We prove the feasibility of the approach in the context of Skype. In particular, we derive and validate a model to predict the Skype QoE as a function of easily measurable network performance metrics. One can see our methodology as a new way of performing measurements in the Internet, where instead of expressing the expected performance in terms of network and device level measurements that only specialists can understand, we express performance in clear terms related to expected quality of experience for different applications.
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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 | 0.001 | 0.000 |
| Open science | 0.002 | 0.004 |
| 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 it