Exploring the accuracy of capturing snapshots in large‐scale P2P IPTV systems
Bibliographic record
Abstract
Summary With the growing maturity of peer‐to‐peer (P2P) technology, Internet protocol television (IPTV) applications based on that gained great success commercially and have attracted more and more attentions from both industry and academia. Currently, the active measurement method based on crawler technology is the most popular and effective one to study P2P IPTV systems. Existing measurement results revealed that accuracy of captured overlay snapshots depends on the crawling speed of crawler system. In order to capture more accurate overlay snapshots of P2P IPTV system, we developed a very fast and efficient distributed crawler system using the distributed architecture and peer degree‐rank mechanism. In this paper, we first introduce the architectures of PPTV channel‐list resource distribution and the whole system, which is the most popular and largest instance of P2P IPTV applications nowadays. Subsequently, this paper evaluates the crawling results of two dedicated crawlers capturing from peer‐list servers and ordinary peers, respectively. Finally, we propose a fast and accurate dedicated crawler system based on distributed architecture and peer degree rank for PPTV. The experiment results show that the performance of our distributed crawler system is much better than other existing crawler systems. Specifically, our distributed crawler can track a very popular channel with about 7200 online users in 30 s. It is also reasonable to believe that our distributed crawler system can capture complete overlay snapshots. To the best of our knowledge, our study work is the first to explore capturing accurate overlay snapshots of large‐scale P2P IPTV applications. Our crawler system can provide a good solution for capturing more accurate overlay snapshots of PPTV system and can also be used to help researchers to design crawler systems for other P2P IPTV systems. Copyright © 2015 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.006 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".