Characterizing SopCast client behavior

Live streaming media applications are becoming more popular each day. Indeed, some important TV channels already broadcast their live content on the Internet. In such scenario, Peer-to-Peer (P2P) applications are very attractive as platforms to distribute live content to large client populations at...

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Veröffentlicht in:Computer communications 2012-05, Vol.35 (8), p.1004-1016
Hauptverfasser: Borges, Alex, Gomes, Pedro, Nacif, José, Mantini, Rodrigo, Almeida, Jussara M., Campos, Sérgio
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Sprache:eng
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Zusammenfassung:Live streaming media applications are becoming more popular each day. Indeed, some important TV channels already broadcast their live content on the Internet. In such scenario, Peer-to-Peer (P2P) applications are very attractive as platforms to distribute live content to large client populations at low costs. A thorough understanding of how clients of such applications typically behave, particularly in terms of dynamic patterns, can provide useful insights into the design of more cost-effective and robust solutions. With the goal of extending the current knowledge of how clients of live streaming applications typically behave, this paper provides a detailed characterization of clients of SopCast, a currently very popular P2P live streaming application. We have analyzed a series of SopCast transmissions collected using PlanetLab. These transmissions are categorized into two different types, namely, major event live transmissions and regular (or non-event) live transmissions. Our main contributions are: (a) a detailed model of client behavior in P2P live streaming applications, (b) the characterization of all model components for two different types of transmissions in the SopCast application, (c) the identification of qualitative and quantitative similarities and differences in the typical client behavior across different transmissions, and (d) the determination of parameter values for the proposed client behavior model to support the design of realistic synthetic workload generators.
ISSN:0140-3664
1873-703X
1873-703X
DOI:10.1016/j.comcom.2012.02.012