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A Digital Fountain Approach to Reliable Distribution of Bulk Data

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1 A Digital Fountain Approach to Reliable Distribution of Bulk Data
John Byers, ICSI Michael Luby, ICSI Michael Mitzenmacher, Compaq SRC Ashu Rege, ICSI

2 Application: Software Distribution
New release of widely used software. Hundreds of thousands of clients or more. Bulk data: tens or hundreds of MB Heterogeneous clients: Modem users: hours Well-connected users: minutes

3 Primary Objectives Scale to vast numbers of clients
No ARQs or NACKs Minimize use of network bandwidth Minimize overhead at receivers: Computation time Useless packets Compatibility Networks: Internet, satellite, wireless Scheduling policies, i.e. congestion control

4 Impediments Packet loss Receiver heterogeneity
wired networks: congestion satellite networks, mobile receivers Receiver heterogeneity packet loss rates end-to-end throughput Receiver access patterns asynchronous arrivals and departures overlapping access intervals

5 Digital Fountain k Source Instantaneous Encoding Stream Transmission k
Received Instantaneous Can recover file from any set of k encoding packets. k Message

6 Digital Fountain Solution
Transmission 5 hours 4 hours 3 hours 2 hours 1 hour 0 hours File User 1 User 2

7 Is FEC Inherently Bad? Faulty Reasoning But… FEC adds redundancy
Redundancy increases congestion and losses More losses necessitate more transmissions FEC consumes more overall bandwidth But… Each and every packet can be useful to all clients Each client consumes minimum bandwidth possible FEC consumes less overall bandwidth by compressing bandwidth across clients

8 DF Solution Features Users can initiate the download at their discretion. Users can continue download seamlessly after temporary interruption. Tolerates moderate packet loss. Low server load - simple protocol. Does scale well. Low network load.

9 Approximating a Digital Fountain
k Source Encoding Time Encoding Stream (1 + c) k Received Decoding Time k Message

10 Approximating a DF: Performance Measures
Time Overhead: Time to decode (or encode) as a function of k. Decoding Inefficiency: packets needed to decode k

11 Work on Erasure Codes Standard Reed-Solomon Codes
Dense systems of linear equations. Poor time overhead (quadratic in k) Optimal decoding inefficiency of 1 Tornado Codes [LMSSS ‘97] Sparse systems of equations. Fast encoding and decoding (linear in k) Suboptimal decoding inefficiency

12 Tornado Z: Encoding Structure
Irregular bipartite graph Irregular bipartite graph k stretch factor = 2 k = 16,000 nodes = source data = redundancy

13 Encoding/Decoding Process

14 Timing Comparison Tornado Z: Average inefficiency = 1.055
Encoding time, 1K packets Reed-Solomon Tornado Z Size 250 K 500 K 1 MB 2 MB 4 MB 8 MB 16 MB 4.6 sec. 19 sec. 93 sec. 442 sec. 30 min. 2 hrs. 8 hrs. 0.11 sec. 0.18 sec. 0.29 sec. 0.57 sec. 1.01 sec. 1.99 sec. 3.93 sec. Decoding time, 1K packets 2.06 sec. 8.4 sec. 40.5 sec. 199 sec. 13 min. 1 hr. 4 hrs. 0.24 sec. 0.31 sec. 0.44 sec. 0.74 sec. 1.28 sec. 2.27 sec. Tornado Z: Average inefficiency = Both codes: Stretch factor = 2

15 Cyclic Interleaving Encoded Blocks Interleaved Encoding Blocks File
Transmission Encoded Blocks Interleaved Encoding Blocks Encoding Copy 1 File Encoding Copy 2 Tornado Encoding

16 Cyclic Interleaving: Drawbacks
The Coupon Collector’s Problem Waiting for packets from the last blocks: More blocks: faster decoding, larger inefficiency T B blocks

17 Scalability over File Size

18 Scalability over Receivers

19 Digital Fountain Prototype
Built on top of IP Multicast. Tolerating heterogeneity: Layered multicast Congestion control [VRC ‘98] Experimental results over MBONE.

20 Research Directions Other applications for digital fountains
Dispersity routing Accessing data from multiple mirror sites in parallel Improving the codes Implementation and deployment Scale to large number of clients Network interactions

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