A comparative study of the SIP and IAX VoIP protocols
Bibliographic record
Abstract
Recently, there has been a strong focus on the development of scalable voice over IP (VoIP) protocols, which are suitable for wide scale deployment. SIP (session initiation protocol) is one such protocol which has been the subject of extensive research over the past few years. More recently, IAX (interasterisk exchange protocol) has emerged as a new VoIP protocol which is steadily gaining credence among the open source community. Among the benefits claimed by the proponents of IAX are its simplicity, NAT-friendliness, efficiency and robustness. This paper makes three key contributions to VoIP research. Firstly, we undertake a comparative evaluation and analysis of the SIP and IAX protocols. Secondly, we report on the viability of utilizing the asterisk PBX as a foundation for conducting research performance studies for VoIP. Finally, we report on live experimental studies of SIP and IAX voice traffic in the Ottawa Metropolitan area. We experimentally studied the performance of voice calls initiated using SIP and IAX for a variety of delay and loss characteristics. In addition, we examined the performance of both protocols in the presence of packet reordering. Our preliminary observations demonstrate that the IAX protocol compares favourably in relation to SIP. More detailed studies are required to evaluate the performance of IAX-based voice traffic in large-scale deployment.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".