Modeling and simulation of SIP tandem server with finite buffer
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Recent collapses of SIP servers (e.g., Skype outage) indicate that the built-in SIP overload control mechanism cannot mitigate overload effectively. We introduce our analytical approach by investigating an overloaded tandem server scenario. Our analytical model: (1) considers a general case that both arrival rate and service rate for signaling messages are generic random processes; (2) makes a detailed analysis of departure processes; (3) allows us to run fluid-based simulations to observe and analyze SIP system performance under some specific scenarios. This approach is 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 computing resources. Our numerical results help us reach a counterintuitive conclusion: A SIP system with a large buffer size may continuously exhibit overload and long queuing delay after experiencing a short period of demand burst or a temporary server slowdown. Small buffer size, on the other hand, can mitigate overload quickly by rejecting a large portion of the requests from a demand burst, and then resume normal operation after a short period of time. Furthermore, numerical results demonstrate that overload at a downstream server may propagate or migrate to its upstream servers and therefore cause widespread server crashes in a real SIP network.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it