Probabilistic Bounds on the End-to-End Delay of Service Function Chains\n using Deep MDN
Bibliographic record
Abstract
Ensuring the conformance of a service system's end-to-end delay to service\nlevel agreement (SLA) constraints is a challenging task that requires\nstatistical measures beyond the average delay. In this paper, we study the\nreal-time prediction of the end-to-end delay distribution in systems with\ncomposite services such as service function chains. In order to have a general\nframework, we use queueing theory to model service systems, while also adopting\na statistical learning approach to avoid the limitations of queueing-theoretic\nmethods such as stationarity assumptions or other approximations that are often\nused to make the analysis mathematically tractable. Specifically, we use deep\nmixture density networks (MDN) to predict the end-to-end distribution of the\ndelay given the network's state. As a result, our method is sufficiently\ngeneral to be applied in different contexts and applications. Our evaluations\nshow a good match between the learned distributions and the simulations, which\nsuggest that the proposed method is a good candidate for providing\nprobabilistic bounds on the end-to-end delay of more complex systems where\nsimulations or theoretical methods are not applicable.\n
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".