Stability condition for SIP retransmission mechanism: Analysis and performance evaluation
Bibliographic record
Abstract
SIP (Session Initiation Protocol) has been widely adopted as a signaling protocol to establish, modify and terminate multimedia sessions between end-users in the Internet. SIP introduces a retransmission mechanism to ensure the reliability of its real-time message delivery. However, retransmission makes server overload worse, as indicated by the recent server crashes in the real carrier networks. In this paper, we use a discrete time model to describe the queuing dynamics of an overloaded SIP server with the retransmission mechanism. We then derive a sufficient stability condition that a SIP server can handle the overload effectively under the retransmission mechanism. Discrete time model allows us to run fluid-based Matlab simulation directly to evaluate the overload performance. This approach is much simpler than event-driven simulation. Event-driven OPNET simulation was also conducted to observe the transient behaviour of an overloaded server in a SIP network. Our simulation results demonstrate that: (1) The sufficient stability bound is quite tight. The bound indicates that effective CPU utilization as low as 20% can still lead to an unstable system after a short period of demand burst or a temporary server slowdown. Resource overprovisioning is not a viable solution to the server crash problem; (2) By satisfying the stability condition, the initial queue size introduced by a transient overload can avoid a system crash. Such stability condition can help the operator to determine whether and when to activate overload control mechanism in case of heavy load.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.003 | 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".