ML Models for Detecting QoE Degradation in Low-Latency Applications: A Cloud-Gaming Case Study
Bibliographic record
Abstract
Detecting abnormal network events is an important activity of Internet Service Providers particularly when running critical applications (e.g., ultra low-latency applications in mobile wireless networks). Abnormal events can stress the infrastructure and lead to severe degradation of user experience. Machine Learning (ML) models have demonstrated their relevance in many tasks including Anomaly Detection (AD). While promising remarkable performance compared to manual or threshold-based detection, applying ML-based AD methods is challenging for operators due to the proliferation of ML models and the lack of well-established methodology and metrics to evaluate them and select the most appropriate one. This paper presents a comprehensive evaluation of eight unsupervised ML models selected from different classes of ML algorithms and applied to AD in the context of cloud gaming applications. We collect cloud gaming Key Performance Indicators (KPIs) time-series datasets in real-world network conditions, and we evaluate and compare the selected ML models using the same methodology, and assess their robustness to data contamination, their efficiency and computational complexity. In addition to the traditional F1-score performance metric used in anomaly detection, we use Matthews Coefficient Correlation (MCC) to better differentiate between models’ efficiencies. Our proposed methodology relies on window-based anomaly detection techniques as they are more useful for network operators compared to single point detection approaches. However, we found most existing window-based approaches to lack in accuracy and may under or over-estimate a model’s performance. Therefore, in this paper, we propose a novel Window Anomaly Decision (WAD) approach that overcomes these drawbacks. We leverage our experimental results to provide insights about the most relevant models for detecting QoE degradation and offer recommendations on their suitability for different application requirements.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".