56 research outputs found
Clustering and Sharing Incentives in BitTorrent Systems
Peer-to-peer protocols play an increasingly instrumental role in Internet
content distribution. Consequently, it is important to gain a full
understanding of how these protocols behave in practice and how their
parameters impact overall performance. We present the first experimental
investigation of the peer selection strategy of the popular BitTorrent protocol
in an instrumented private torrent. By observing the decisions of more than 40
nodes, we validate three BitTorrent properties that, though widely believed to
hold, have not been demonstrated experimentally. These include the clustering
of similar-bandwidth peers, the effectiveness of BitTorrent's sharing
incentives, and the peers' high average upload utilization. In addition, our
results show that BitTorrent's new choking algorithm in seed state provides
uniform service to all peers, and that an underprovisioned initial seed leads
to the absence of peer clustering and less effective sharing incentives. Based
on our observations, we provide guidelines for seed provisioning by content
providers, and discuss a tracker protocol extension that addresses an
identified limitation of the protocol
Modeling and Control of Rare Segments in BitTorrent with Epidemic Dynamics
Despite its existing incentives for leecher cooperation, BitTorrent file
sharing fundamentally relies on the presence of seeder peers. Seeder peers
essentially operate outside the BitTorrent incentives, with two caveats: slow
downlinks lead to increased numbers of "temporary" seeders (who left their
console, but will terminate their seeder role when they return), and the
copyright liability boon that file segmentation offers for permanent seeders.
Using a simple epidemic model for a two-segment BitTorrent swarm, we focus on
the BitTorrent rule to disseminate the (locally) rarest segments first. With
our model, we show that the rarest-segment first rule minimizes transition time
to seeder (complete file acquisition) and equalizes the segment populations in
steady-state. We discuss how alternative dissemination rules may {\em
beneficially increase} file acquisition times causing leechers to remain in the
system longer (particularly as temporary seeders). The result is that leechers
are further enticed to cooperate. This eliminates the threat of extinction of
rare segments which is prevented by the needed presence of permanent seeders.
Our model allows us to study the corresponding trade-offs between performance
improvement, load on permanent seeders, and content availability, which we
leave for future work. Finally, interpreting the two-segment model as one
involving a rare segment and a "lumped" segment representing the rest, we study
a model that jointly considers control of rare segments and different uplinks
causing "choking," where high-uplink peers will not engage in certain
transactions with low-uplink peers.Comment: 18 pages, 6 figures, A shorter version of this paper that did not
include the N-segment lumped model was presented in May 2011 at IEEE ICC,
Kyot
Decentralized Adaptive Helper Selection in Multi-channel P2P Streaming Systems
In Peer-to-Peer (P2P) multichannel live streaming, helper peers with surplus
bandwidth resources act as micro-servers to compensate the server deficiencies
in balancing the resources between different channel overlays. With deployment
of helper level between server and peers, optimizing the user/helper topology
becomes a challenging task since applying well-known reciprocity-based choking
algorithms is impossible due to the one-directional nature of video streaming
from helpers to users. Because of selfish behavior of peers and lack of central
authority among them, selection of helpers requires coordination. In this
paper, we design a distributed online helper selection mechanism which is
adaptable to supply and demand pattern of various video channels. Our solution
for strategic peers' exploitation from the shared resources of helpers is to
guarantee the convergence to correlated equilibria (CE) among the helper
selection strategies. Online convergence to the set of CE is achieved through
the regret-tracking algorithm which tracks the equilibrium in the presence of
stochastic dynamics of helpers' bandwidth. The resulting CE can help us select
proper cooperation policies. Simulation results demonstrate that our algorithm
achieves good convergence, load distribution on helpers and sustainable
streaming rates for peers
Clustering and Sharing Incentives in BitTorrent Systems
Peer-to-peer protocols play an increasingly instrumental role in Internet content distribution. Consequently, it is important to gain a full understanding of how these protocols behave in practice and how their parameters impact overall performance. We present the first experimental investigation of the peer selection strategy of the popular BitTorrent protocol in an instrumented private torrent. By observing the decisions of more than 40 nodes, we validate three BitTorrent properties that, though widely believed to hold, have not been demonstrated experimentally. These include the clustering of similar-bandwidth peers, the effectiveness of BitTorrent's sharing incentives, and the peers' high average upload utilization. In addition, our results show that BitTorrent's new choking algorithm in seed state provides uniform service to all peers, and that an underprovisioned initial seed leads to the absence of peer clustering and less effective sharing incentives. Based on our observations, we provide guidelines for seed provisioning by content providers, and discuss a tracker protocol extension that addresses an identified limitation of the protocol
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