Results 1 -
7 of
7
Playing with the bandwidth conservation law
- in IEEE P2P
"... We investigate performance bounds of P2P systems by application of the law of bandwidth conservation. This approach is quite general and allows us to consider various sharing systems such as fixed-rate streaming, VoD-type streaming, and elastic file sharing. Starting from a general law of bandwidth ..."
Abstract
-
Cited by 11 (2 self)
- Add to MetaCart
(Show Context)
We investigate performance bounds of P2P systems by application of the law of bandwidth conservation. This approach is quite general and allows us to consider various sharing systems such as fixed-rate streaming, VoD-type streaming, and elastic file sharing. Starting from a general law of bandwidth conservation, we consider several specific cases that apply to various P2P systems. For dynamic systems with a stationary arrival process, we show that simple seeding policies result in regimes where the download rates are arbitrarily fast. We consider a case with equal download rate among all peers as well as cases where the download rate is a function of upload rates, inspired by Bit-Torrent’s Tit-for-Tat policy. In particular, we show that the sustainable proportion of free-riders is closely related to the Tit-for-Tat parameter. 1
An Upload Bandwidth Threshold for Peer-to-Peer Video-on-Demand Scalability
, 2009
"... We consider the fully distributed Video-on-Demand problem, where n nodes called boxes store a large set of videos and collaborate to serve simultaneously n videos or less between them. It is said to be scalable when Ω(n) videos can be distributively stored under the condition that any sequence of de ..."
Abstract
-
Cited by 3 (0 self)
- Add to MetaCart
(Show Context)
We consider the fully distributed Video-on-Demand problem, where n nodes called boxes store a large set of videos and collaborate to serve simultaneously n videos or less between them. It is said to be scalable when Ω(n) videos can be distributively stored under the condition that any sequence of demands for these videos can always be satisfied. Our main result consists in establishing a threshold on the average upload bandwidth of a box, above which the system becomes scalable. We are thus interested in the upload bandwidth video bitrate normalized upload capacity u = of a box. The number m of distinct videos stored in the system is called its catalog size. We show an upload capacity threshold of 1 for scalability in a homogeneous system, where all boxes have the same upload capacity. More precisely, a system with u < 1 has constant catalog size m = O(1) (every box must store some data of every video). On the other hand, for u> 1, an homogeneous system where all boxes have same upload capacity at least u admits a static allocation of m = Ω(n) videos into the boxes such that any adversarial sequence of video demands can be satisfied. Moreover, such an allocation can be obtained randomly with high probability. This result is generalized to a system of boxes that have heterogeneous upload capacities under some balancing conditions.
Fine Tuning of a Distributed VoD System
, 2009
"... In a distributed Video-on-Demand system, customers are in charge of storing the video catalog, and they actively participate in serving video requests generated by other customers. The design of such systems is driven by key constraints like customer upload and storage capacities, video popularity ..."
Abstract
- Add to MetaCart
In a distributed Video-on-Demand system, customers are in charge of storing the video catalog, and they actively participate in serving video requests generated by other customers. The design of such systems is driven by key constraints like customer upload and storage capacities, video popularity distribution, and so on. In this paper, we analyze by simulations the impact of: i) the video allocation technique (used for distributed storage) ii) the use of a cache that allows nodes to redistribute the video they are using iii) the use of static/dynamic algorithms for video distribution. Based on these results, we provide some guidelines for setting the system parameters: the use of cache strongly improves system performance; popularity based allocation techniques can be sensitive and bring little improvement; dynamic distribution algorithms are needed only in extreme scenarios while static ones are generally sufficient.
3.1.2. Small World Phenomenon 2 3.1.3. Doubling Metrics 2
"... c t i v it y e p o r t 2009 Table of contents ..."
(Show Context)
Project-Team Gang Graphs, networks and algorithms
"... c t i v it y e p o r t 2007 Table of contents ..."
(Show Context)
