Modeling and design of a Session Initiation Protocol overload control algorithm
Bibliographic record
Abstract
Recent collapses of Session Initiation Protocol (SIP) servers indicate that the built-in SIP overload control mechanism cannot mitigate overload effectively. In this paper, we propose a new SIP overload control algorithm by introducing a novel analytical approach to model the dynamic behavior of a SIP network where each server has a finite buffer. Three key breakthroughs of our modeling approach are the formulations of the message loss process, message retransmission process, and the complex departure process through detailed analysis. Our modeling results indicate that retransmissions triggered by the queuing delay are redundant, thus we propose a feedback control mechanism that regulates the retransmission message rate to mitigate the overload. We then demonstrate how to extend our analytical approach to the modeling of our overload control solution. Simulation based on this analytical model runs much faster than event-driven simulation, which needs to track thousands of retransmission timers for outstanding messages and may crash a simulator due to limited computation resources. Performance evaluation demonstrates that: (1) without the control algorithm applied, the overload at a downstream server may propagate to its upstream servers and cause widespread network failure; (2) in the case of short-term overload, our feedback control solution can mitigate the overload effectively without rejecting calls intentionally or reducing network utilization, thus avoiding the disadvantages of existing overload control solutions. In addition, compared with the pushback solution, our retransmission-based solution achieves a better trade-off between the speed to cancel the overload and the call rejection rate when an overload lasts a short period.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".