Measurements, analysis, and modeling of BitTorrent-like systems
By Lei Guo, Songqing Chen, Zhen Xiao, Enhua Tan, Xiaoning Ding, Xiaodong Zhang. ACM/SIGCOMM Internet Measurement Conference, 2005
451 citations recorded by the source corpus.
Subfields: Computer Networks and Communications
View Paper: https://www.semanticscholar.org/paper/ab4f156bbba2e6083d81b0408e76b1d01753bf04
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Networks and Communications
- Andreas Haeberlen
- Paarijaat Aditya
- Bruce MacDowell Maggs
- Peter Druschel
- Arvind Krishnamurthy
- Xiaodong Zhang
- Guillaume Urvoy-Keller
- Lei Guo
Citations in the corpus
- Clustering and sharing incentives in BitTorrent systems (2006, background): “Furthermore, most measurement studies h ave evaluated peers connected to public torrents—BitTorrent download sessions [11, 12, 20].”
- Do incentives build robustness in bit torrent (2007, background): “Users may choose to reallocate capacity to services other than BitTorrent or to other swarms, as most peers participate in several swarms simultaneously [7].”
- Improving Traffic Locality in BitTorrent via Biased Neighbor Selection (2006, methodology): “In the following discussion, we refer to the BitTorrent network using uniform random neighbor selection as “regular BitTorrent”, and the BitTorrent network using biased neighbor selection as “biased BitTorrent”.”
- Peer-assisted content distribution in Akamai netsession (2013, methodology): “When a user attempts to download an object that is distributed using the DLM, she is first asked to install the NetSession Interface on her computer (unless it is already available from previous usage).”
- Analyzing patterns of user content generation in online social networks (2009, background): “The user participation in terms of active time in peer-topeer and social networks has been assumed to follow exponential distributions in modeling [9, 16], and it has been also assumed that there is strong correlation between user active time and user contribution.”
- Rarest first and choke algorithms are enough (2006, background): “Our findings significantly differ from previous work [5,10, 11, 13, 15, 18].”