An End-to-End Probabilistic Network Calculus with Moment Generating\n Functions
Bibliographic record
Abstract
Network calculus is a min-plus system theory for performance evaluation of\nqueuing networks. Its elegance stems from intuitive convolution formulas for\nconcatenation of deterministic servers. Recent research dispenses with the\nworst-case assumptions of network calculus to develop a probabilistic\nequivalent that benefits from statistical multiplexing. Significant\nachievements have been made, owing for example to the theory of effective\nbandwidths, however, the outstanding scalability set up by concatenation of\ndeterministic servers has not been shown.\n This paper establishes a concise, probabilistic network calculus with moment\ngenerating functions. The presented work features closed-form, end-to-end,\nprobabilistic performance bounds that achieve the objective of scaling linearly\nin the number of servers in series. The consistent application of moment\ngenerating functions put forth in this paper utilizes independence beyond the\nscope of current statistical multiplexing of flows. A relevant additional gain\nis demonstrated for tandem servers with independent cross-traffic.\n
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".