Bibliographic record
Abstract
The network calculus is a framework for the analysis of communication networks, which exploits that many computer network models become tractable for analysis if they are expressed in a min-plus or max-plus algebra. In a min-plus algebra, the network calculus characterizes amounts of traffic and available service as functions of time. In a max-plus algebra, the network calculus works with functions that express the arrival and departure times or the required service time for a given amount of traffic. While the min-plus network calculus is more convenient for capacity provisioning in a network, the max-plus network calculus is more compatible with traffic control algorithms that involve the computation of timestamps. Many similarities and relationships between the two versions of the network calculus are known, yet they are largely viewed as distinct analytical approaches with different capabilities and limitations. We show that there exists a one-to-one correspondence between the min-plus and max-plus network calculus, as long as traffic and service are described by functions with real-valued domains and ranges. Consequently, results from one version of the network calculus can be readily applied for computations in the other version. The ability to switch between min-plus and max-plus analysis without any loss of accuracy provides additional flexibility for characterizing and analyzing traffic control algorithms. This flexibility is exploited for gaining new insights into link scheduling algorithms that offer rate and delay guarantees to traffic flows.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".