A Simple Model for Analyzing P2P Streaming Protocols

Zhou, Yipeng; Chiu, Dah Ming; Lui, John C. S.

doi:10.1109/icnp.2007.4375853

Cited by 169 publications

(144 citation statements)

References 9 publications

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“…This leads to the following expression for the probability s i : Figure 2 shows graphically the Rarest First and Greedy strategies. It is well known that Greedy achieves low latency, but it is not as scalable as Rarest First [25], [23]. A mixture of these two strategies is possible, by simply dividing the buffer in two parts, from positions 1 to some index value m, 1 ≤ m ≤ N , and from m + 1 to N , and by and applying the Rarest First strategy in the first section and Greedy in the second one.…”

Section: Classical Strategies and A Mixturementioning

confidence: 99%

“…, N } into s i ∈ [0, 1] is called the selection function of the strategy. Defining the index of the initial piece of the server as 1, it is shown in [23] that:…”

Section: Simple Model For Sharing (From [23])mentioning

confidence: 99%

“…The paper is organized as follows. Section II describes a simple model of piece selection strategies proposed in [23]. In Section III some popular piece selection strategies are presented.…”

Section: Introductionmentioning

confidence: 99%

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A COP for cooperation in a P2P streaming protocol

Bertinat

Padula

Amoza

et al. 2009

2009 International Conference on Ultra Modern Telecommunications &Amp; Workshops

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Abstract-This paper introduces a Combinatory Optimization Problem (COP) which captures the performance in cooperation of a P2P Streaming Network, considered at the buffer level. A new family of strategies of cooperation is defined, which represents the order of query of pieces in a communication between peers. We define a measure of quality for each strategy, based on the most shocking video streaming parameters, namely the continuity and the start-up latency. Our aim is o find quasi-optimal strategies inside the proposed family, by means of on a model based on an Asymmetric Traveling Salesman Problem (ATSP). Finally, we solve this ATSP using an ACO (Ant Colony Optimization)-based algorithm. The results show substantial improvement with this approach, once compared with the ones obtained by previous defined strategies of cooperation.

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Section: Classical Strategies and A Mixturementioning

confidence: 99%

“…, N } into s i ∈ [0, 1] is called the selection function of the strategy. Defining the index of the initial piece of the server as 1, it is shown in [23] that:…”

Section: Simple Model For Sharing (From [23])mentioning

confidence: 99%

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A COP for cooperation in a P2P streaming protocol

Bertinat

Padula

Amoza

et al. 2009

2009 International Conference on Ultra Modern Telecommunications &Amp; Workshops

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“…Secondly, missing chunks may be acceptable if a resilient codec is used, so optimal values are not always comparable to those in the file transfer case. Then, the buffer size, which is a parameter specific to streaming, can impact the performance (see for instance [12]). …”

Section: Introductionmentioning

confidence: 99%

On Optimizing for Epidemic Live Streaming

Hegde

Mathieu

Perino

2010

2010 IEEE International Conference on Communications

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Abstract-Optimal dissemination schemes have previously been studied for peer-to-peer live streaming applications. Live streaming being a delay-sensitive application, fine tuning of dissemination parameters is crucial. In this paper, we investigate optimal sizing of chunks, the units of data exchange, and probe sets, the number peers a given node probes before transmitting chunks. Chunk size can have significant impact on diffusion rate (chunk miss ratio), diffusion delay, and overhead. The size of the probe set can also affect these metrics, primarily through the choices available for chunk dissemination. We perform extensive simulations on the so-called random-peer, latest-useful dissemination scheme. Our results show that size does matter, with the optimal size being not too small in both cases.

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“…Other work have relied on high-level models that can be evaluated via analysis and simulation [13]- [19]. While clearly important for advancing our understanding, these kinds of models or tools typically do not capture realistic network cross traffic or lower-level system issues in each peer (e.g., Fig.…”

mentioning

confidence: 99%

Proxy-P2P Streaming under the Microscope: Fine-Grain Measurement of a Configurable Platform

Jiang

Chiang

Rexford

et al. 2010

2010 Proceedings of 19th International Conference on Computer Communications and Networks

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Abstract-Although peer-to-peer (P2P) streaming can efficiently deliver live video content to large user populations, existing applications often suffer from limited video quality, periodic hiccups, and high delays. To overcome some of the limitations of today's unstructured (mesh-based) designs, we have developed and deployed FastMesh-SIM, a novel P2P streaming system that leverages proxies, push-mechanism and IP multicast to achieve lower playback delay and better stream continuity. Having control over a real P2P streaming system also gives us a rare opportunity to conduct controlled experiments where we vary major design parameters (e.g., push vs. pull delivery, IP multicast support, streaming rate, and video segment size) under a range of operating conditions (e.g., dynamics of peer churn, and different network configurations), while collecting detailed, fine-granular measurements (e.g., the various components of endto-end delay). Analysis of the measurement data, consisting of seven trials of streaming several live TV channels for more than 100 hours to 140 peers, sheds light on how design decisions and the operating environment affect important performance metrics. Our experiments show that a push-based, proxy-P2P system can achieve low delay and good video quality, though network bottlenecks on long-haul connections can sometimes cause disruptions in a global deployment. Theory-practice gaps observed from the data are also discussed. Large-scale, global experiments are now being carried out.I. INTRODUCTION Unstructured (or mesh-based) peer-to-peer (P2P) systems, where peers asynchronously swap segments of the video, can substantially reduce server bandwidth and system cost [1], though often at the cost of high latency and periodic hiccups [2]- [4]. An attractive alternative for efficient, low-latency video streaming is to explicitly organize the peers into an application-level multicast tree, and use proxies and recovery protocols to enhance streaming quality. Still, many important questions remain about how to most effectively design and operate a tree-based P2P streaming system. In this paper, we perform an in-depth measurement study of a proxy-P2P network, FastMesh-SIM [5], [6], which broadcasts live TV channels and is deployed in Hong Kong and Princeton.

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A Simple Model for Analyzing P2P Streaming Protocols

Cited by 169 publications

References 9 publications

A COP for cooperation in a P2P streaming protocol

A COP for cooperation in a P2P streaming protocol

On Optimizing for Epidemic Live Streaming

Proxy-P2P Streaming under the Microscope: Fine-Grain Measurement of a Configurable Platform

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