CS5248 Student Presentation 1

Scalable Resilient Media Streaming

Suman Banerjee, Seungjoon Lee, Ryan Braud, Bobby Bhattacharjee, Aravind Srinivasan

NOSSDAV 2004

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Application Layer Multicast

Advantages No change in network infrastructure Applications have full control

Disadvantages Stretch and Stress Control data overhead

Multicast forwarding at end-hosts Construct an overlay network

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Examples

Narada Builds a mesh, then a tree Everybody knows everybody High control overhead

NICE Hierarchical clustering of nodes Low control overhead

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NICE

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Problem

Overlay network node failures Overlay network link failures Congestion failures

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SRMS Architecture

M S

AR

B

Y

X

Media Stream

Join Request

Address of Sender

Request Data

Data

Streaming Server SRMS sender

SRMS-RP

SRMS client

SRMS client

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Probabilistic Resilient Multicast (PRM)

Randomized Forwarding Triggered NAKs

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Randomized Forwarding

Each node in the overlay network forwards the data to a constant number of other overlay nodes with a low probability (0.01–0.03)

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Randomized Forwarding (cont’d)

A

B

D

C

FE

NML

G H

QPOKJI

X X

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Overhead Analysis

n : Total number of nodesr : Number of randomly forwarded nodesβ : Probability of random forwardingPer-node overhead of PRM: βr

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Triggered NAKs

Data losses due to link errors and network congestion are recovered using NAK-based retransmissions using the missing sequence numbers.

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Triggered NAKs (cont’d)

• Each node piggybacks a bit mask with every forwarded packet indicating the prior sequence numbers it has correctly received

• Recipient of the data packet detects missing packets using the gaps in the received sequence and requests appropriate retransmissions

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Triggered NAKs (cont’d)

0 1 1 10 0 1 1

0 1 1 1X

Y

Z

SEQ: 18

SEQ: 18

NAK: 14, 15

NAK: 16

0 0 0 00 0 1 1

17 16 15 14

17 16 15 14

17 16 15 14

17 16 15 14

17 16 15 14

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Experiments

n : 10 – 10,000r : 1 - 3β : 0.01 – 0.03

Compared PRM with Best-Effort (BE) methods

Nomenclature: PRM b (r, β)

b – bit mask used in NAK retransmissions

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Evaluations: Delivery Ratio

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Evaluations: Data Loss

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Evaluations: End-to-End Latency

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Conclusions

SRMS achieves high data distribution rates even with node and link failures Very low overhead Scales very well

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Q&A