Name
Abstract | ||
|---|---|---|
Peer-to-peer (P2P) live streaming services are getting more popular as the average link capacity in the Internet becomes greater for end hosts. In order to provide a high quality P2P live streaming service, it is essential to handle user dynamics effectively to mitigate streaming disruptions caused by user churn. We collect massive user traces from the largest P2P live streaming site in Korea, namely, Afreeca. Based on the trace results obtained, we analyze user dynamics in the service. In particular, we analyze the user behaviors in terms of three criteria: long-stay users, short-stay users, and user churn. (1) The results show that a certain amount of long-stay users exist in a live streaming session, and it is challenging to recognize the long-stay users online, which will help to provide more resilient live streaming service. (2) Around 20% of the users stay in a session shorter than 1% of the session length. Short-stay users incur high control overhead and we discuss how to efficiently handle the short-stay users. (3) By intentionally disrupting a video streaming, we closely observe the users' leaving behavior and suggest the acceptable recovery time to keep the users remaining the session. We believe our findings can be useful not only to P2P live streaming systems, but also to every live streaming systems. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ICC.2010.5502009 | ICC |
Keywords | Field | DocType |
p2p live streaming services,average link capacity,user dynamics,afreeca,internet,video streaming,peer-to-peer computing,silicon,computer science,fluctuations,system analysis and design,tv,p2p | Structured systems analysis and design method,Computer science,Video streaming,Computer network,Peer to peer computing,Live streaming,Multimedia,Communications society,The Internet | Conference |
ISSN | ISBN | Citations |
1550-3607 | 978-1-4244-6402-9 | 8 |
PageRank | References | Authors |
0.66 | 5 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kunwoo Park | 1 | 136 | 14.51 |
| Dukhyun Chang | 2 | 96 | 7.04 |
| Junghoon Kim | 3 | 8 | 0.66 |
| Wonjun Yoon | 4 | 11 | 1.19 |
| Taekyoung Kwon | 5 | 1894 | 153.24 |