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 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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".