CURSA-SQ: A Methodology for Service-Centric Traffic Flow Analysis
Bibliographic record
Abstract
The rapid availability of new services means that network operators cannot exhaustively test their impact on the network or anticipate any capacity exhaustion. This situation will be worse with the imminent introduction of 5G technology and the kind of totally new services that it will support. In addition, the increasing complexity of the network makes analyzing its behavior challenging against the specific traffic that needs to be supported; this prevents from training human operators and, much less, machine learning algorithms that might automatize network operation. In this paper, we present CURSA-SQ, a methodology to analyze network behavior when specific traffic that would be generated by groups of service consumers is injected. CURSA-SQ includes input traffic flow modeling with second and sub-second granularity based on specific service and consumer behavior, as well as a continuous G/G/1/k queue model based on the logistic function. The methodology allows for accurately studying the traffic flows at the input and outputs of complex scenarios with multiples queue systems, as well as other metrics such as delays, while showing noticeable scalability. Application use cases include packet and optical network planning, service introduction assessment, and autonomic networking, just to mention a few.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".