KEYWORDS: Statistical analysis, Data modeling, Computing systems, Computer simulations, Receivers, Systems modeling, System identification, Computer science, Information science, Video
We describe a practical auditing approach designed to encourage fairness in peer-to-peer streaming. Auditing
is employed to ensure that correct nodes are able to receive streams even in the presence of nodes that do not
upload enough data (opportunistic nodes), and scales well when compared to previous solutions that rely on
tit-for-tat style of data exchange. Auditing involves two roles: local and global. Untrusted local auditors run on
all nodes in the system, and are responsible for collecting and maintaining accountable information regarding
data sent and received by each node. Meanwhile, one or more trusted global auditors periodically sample the
state of participating nodes, estimate whether the streaming quality is satisfactory, and decide whether any
actions are required. We demonstrate through simulation that our approach can successfully detect and react to
the presence of opportunistic nodes in streaming sessions. Furthermore, it incurs low network and computational
overheads, which remain fixed as the system scales.
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