Controlling Retransmission Rate for Mitigating SIP Overload
Bibliographic record
Abstract
With rapidly growing deployment, SIP has become a main signaling protocol for IP telephony and multimedia sessions in the Internet. SIP employs a retransmission mechanism to maintain its reliability. Recent server collapse due to emergency-induced call volume in carrier networks indicates that message retransmissions triggered by various SIP timers make the overload worse. The built-in overload control mechanism cannot handle overload conditions effectively. Since the retransmissions caused by the overload introduce more overhead rather than reliability, we suggest mitigating the overload by reducing the retransmission rate. We propose a novel algorithm to detect the potential overload at the downstream servers and control retransmission message rate from upstream servers to mitigate the overload at the downstream servers. We investigate two typical overload scenarios caused by demand burst and server slow down respectively. OPNET simulations demonstrate that (1) the proposed solution can help the overloaded downstream server to cancel its overload effectively after it resumes its normal operation status; (2) without the overload control algorithm applied, the overload at the downstream server may propagate or migrate to its upstream servers.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".