Analysis of SIP retransmission probability using a Markov-Modulated Poisson Process model
Bibliographic record
Abstract
As a main signaling protocol for multimedia sessions in the Internet, SIP (Session Initiation Protocol) introduces a retransmission mechanism to maintain the reliability for its realtime transmission. However, retransmission will make the server overload worse. Recent collapse of SIP servers due to emergencyinduced call volume indicates that the built-in SIP overload control mechanism cannot prevent the server from overload collapse under heavy load. In this paper, we apply a MMPP (Markov-Modulated Poisson Process) model to analyze the queuing mechanism of SIP server under two typical service states. The MMPP model allows us to investigate the probability of SIP retransmissions. By performing numerous experiments statistically to verify SIP retransmission probability calculated by MMPP model, we find that high retransmission probability caused by short demand surge or reduced server processing capacity during maintenance period may overload and crash a server. We run simulations using time-series directly to observe and analyze the system performance of an overloaded SIP server. This is much faster than event-driven simulation. Numerical results demonstrate that low resource utilization corresponds to low retransmission probability. However, a utilization as low as 20% cannot always guarantee a SIP system stability upon a temporal server slowdown or a short period of demand burst.
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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 | 0.003 | 0.007 |
| 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 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".