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Log in with Facebook Log in with Google. Remember me on this computer. Enter the email address you signed up with and we'll email you a reset link. Need an account? Click here to sign up. Download Free PDF. Fabien Mathieu. A short summary of this paper. Download Download PDF. Translate PDF. They allow to leverage the capacities of a P2P system. While seeding is a natural idea for fileshar- ing or video-on-demand applications, it seems somehow counter-intuitive in the context of live streaming.
This paper aims at describing the feasibility and performance of P2P live seeding. We then propose a linear overhead model and extend the results for this model, for a single seeder and for a set of seeders as well it is not always possible to perfectly aggregate individual efficiencies in a given system.
On the one hand, the democra- tization of very high speed, symmetric, Internet access like FTTH is expected to improve the upload capacity of P2P systems, but on the other hand the evo- lution of content quality standards makes the requirements in terms of content size and rate higher and higher: earlier video feeds on the Internet where low quality, requiring streamrates of a few hundred kbps, whereas HDTV implies rate of up to 20 Mbps, possibly more with the upcoming of 3D video content.
Using seeders is quite natural for file-sharing or Video-on-Demand: after a peer has downloaded its file or video, it becomes a potential seeder for that content. Therefore, for a peer to act as a seeder, it has to receive at least a part of the corresponding content, which it does not want to watch by definition. This generic theoretical framework can be used to derive simple dimensioning rules and rec- ommendations for the design of P2P live streaming with seeders.
We provide explicit, tight, upper bounds for efficiency, taking the overhead explicitly into account. We also address the aggregation issues that come from using several seeders. We give conditions and simple diffusion schemes that allow to nearly achieve the theoretical bounds, and provide a few simple examples that illustrate the potential of our findings.
Remark focusing on a single scenario live streaming and a single type of peer seeders was a deliberate choice, in order to get a clean framework for investigating theoretical performance, especially with regards to the overhead modeling aspects.
This does not preclude of possible extensions of the approach presented here to other use cases. The related work with respect to P2P bandwidth dimensioning is briefly exposed in Section 3. Section 5 proposes a preliminary study of efficiency for two overhead-free models.
This study is a starting point for the main results of this paper, which derive the efficiency of seeders in a model with explicit overhead Section 6.
The validity conditions and applications of the results are discussed in Section 7. Section 8 concludes. The delivery is handled by a P2P live streaming system. The specificity of live streaming is that the content cannot be prefetched. A play-out buffer may tolerate some jitter, but the live constraints usually limit the size of that buffer to less than a few seconds, so a conservative, yet realistic assumption is that content must received at exactly the rate r during the whole watching experience.
To compare with, filesharing usually requires no minimal rate, while in the case of Video-on-Demand, content may be prefetched at a rate greater than r. Remark we do not focus on the way seeders could be enforced in a real live streaming system. That kind of policy can be enforced through penalties no service guarantee, reduced catalog and rewards higher QoS, access to premium content. We denote by C, L and S the sets of servers, leechers and seeders respectively. The number of leechers resp.
We assume that the download capacity is always sufficient to support the content rate r and a possible overhead. Note that the bandwidth distribution of the seeders may differ from the one of the leechers.
One the other hand, seeders deployed by some content provider should probably have higher bandwidths. A diffusion scheme for the system is a policy that describes how the content is distributed. Of course, choosing which redundant data is treated as overhead is arbitrary. We also propose two simpler models that will serve for didactic purposes: perfect systems and limited fanout systems. NET, code System. ArgumentException: An item with the same key has already been added.
This is due to a configuration issue on the host machine where multiple font files with the same name exist and can't be added to the PDF Generator internal dictionary due to unique name constraints. This issue has been worked around in this latest release. See Unicode example for usage help. Note: This is currently an undocumented feature.
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