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transcript
On Large-Scale Peer-to-Peer Streaming Systems with Network
Coding
Chen Feng, Baochun LiDept. of Electrical and Computer
Engineering University of Toronto
Presentation by: Shabnam Mirshokraie
ACM Multimedia 2008
Peer to Peer Streaming Challenges How do we maintain a high playback
quality at all participating peers? How do we improve user experience
with the shortest initial buffering delay? How do we minimize server bandwidth
costs? How do we design a system that is
resilient to peer dynamics?
Related Work On achieving minimum delay
A centralized algorithm [Y. Liu ACM Multimedia 07]
On achieving maximum streaming rate A decentralized algorithm [Massoulie et al.
INFOCOM 07] On achieving near optimal steaming
rate and delay Several decentralized algorithms [Bonald et
al. SIGMETRICS 08]
Related Work (Cont.)
None of them is actually implemented by system designers No performance guarantees on
resilience Centralized algorithms go nowhere Complexity and overhead issues
Practical Implementation
Mesh based pull streaming strategies A live stream is divided into segments Segments arrive at a peer in roughly
sequential order
Mesh Based Pull Streaming (Cont.)
Advantages Simplicity of implementation Better resilience to peer dynamics
Disadvantages Significant overhead of requests and
buffer availability exchanges Longer initial buffering delays
Designing a Good P2P Streaming System
Simple to implement Low protocol overhead With theoretical guarantees on
Smooth playback Short initial buffering delay Low server bandwidth costs Resilience to peer dynamics
Streaming With Network Coding
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Random Push on a Random Mesh (Cont.) Traditional pull based strategies
Large buffer map size Frequently buffer map exchanges Explicit requests messages
Random push with random Network Coding Smaller buffer map Less frequent buffer map exchanges No need for explicit request messages
Synchronized Playback Synchronized playback buffers on all peers
All peers play the same segment at approximately the same time
Playback buffers overlap as much as possible The new peer skips a few segments
Receiving segments that are D seconds after the current point of playback
The duration of D seconds corresponds to the initial buffering delay
Performance Analysis of Coding Quantitative answers to the following
questions What are the sufficient conditions for Coding
to achieve good overall performance? How far from optimality is the performance
of Coding? Exploring the performance gap between
Coding and optimal streaming scheme Motivation for more elaborated designs
System Model and Notations
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System Model and Notations (Cont.) Flash crowd scenario
most of the peers join the system in a short time period
Highly dynamic scenario peers join and leave the system in a
highly volatile Fashion (peer churn)
Flash Crowd Scenarios
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Flash Crowd Scenarios (Cont.) Theorem 1 establishes sufficient
conditions on smooth playback Heterogeneity in upload capacity in not an
issue in Coding
High bandwidth utilization
Flash Crowd Scenarios (Cont.)
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Flash Crowd Scenarios (Cont.) Apply Theorem 1 to understand the gap
between Coding and optimal streaming
Theorem 2 demonstrates that Coding is near optimal in terms of sustainable streaming rate during a flash crowd.
Flash Crowd Scenarios (Cont.)
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Flash Crowd Scenarios (Cont.) Theorem 3 shows that Coding manages
to guarantee very short initial buffering delays during a flash crowd.
Theorem 2 and Theorem 3 suggest that the performance gap between Coding and optimal streaming scheme is small.
Flash Crowd Scenarios (Cont.)
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Highly Dynamic Scenarios
The arrivals of new peers in current time No effect on the playback quality of the
most urgent segment
The departures of existing peers Central role in the playback quality
Highly Dynamic Scenarios (Cont.)
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Theoretical and simulation results for relative additional server capacity to handle peer dynamics
in the worst case
The theoretical bound is tight when bandwidth supply barely exceeds bandwidth demand, while the bound is loose when supply outstrips demand.
Simulation results for relative additional server capacityto handle peer dynamics in the average case
Only a small amount of additional server capacity is required, even when 50% peers leave the system.
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Formal Proof of Sufficient Conditions-Theorem 1.
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Formal Proof of Sufficient Conditions-Theorem 1. (Cont.)
Fraction of Redundant Blocks
Linearly dependent coded blocks from upstream peers Waste of bandwidth resources
Estimation of the fraction of redundant blocks Bandwidth utilization of Coding
Fraction of Redundant Blocks (Cont.)
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Fraction of Redundant Blocks (Cont.)
With high probability any coded block from an upstream peer is useful to its downstream peer The space spanned by the coded blocks on
the upstream peer is not a subspace of the space spanned on downstream peer.
Fraction of Redundant Blocks (Cont.)
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Fraction of Redundant Blocks (Cont.) The randomized encoding algorithm on an
upstream peer does not take into account the coded blocks accumulated on its downstream peers
Producing some redundant coded blocks Size of the Galois field q
Upstream peer has no innovative coded blocks for its downstream peers
The probability of such event is small The random push operations naturally create
sufficient diversity
Simulation results for fraction of redundant coded blocks
The fraction of redundancy induced by network coding is in the order of 0.001, even when the field size q is as small as 64
Comparison with PullA comparison of playback quality between Coding and Pull
under different peer dynamic scenarios
The change of playback quality over time in Coding and Pull under a typical flash crowd
scenario and a highly dynamic scenario
Pull than dynamicspeer under
stabilitybetter much has Coding that shows comparison The
Summary Analytically investigation of the
performance of streaming systems with network coding Simple and effective streaming
Extensive large scale simulations The analytical results have been validated
Demonstrating the advantages of network coding based protocols over traditional pull based streaming protocols.