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

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

7 Is FEC Inherently Bad? Faulty Reasoning 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 Stream Encoding Time (1 + c) k k Received Message Decoding Time

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 a b c d a b f a b c d g c e g h b d e f g h e f g h

14 Timing Comparison Encoding time, 1K packets Size Reed-Solomon Tornado Z 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 sec sec sec sec sec sec sec. Decoding time, 1K packets Size Reed-Solomon Tornado Z 250 K 500 K 1 MB 2 MB 4 MB 8 MB 16 MB 2.06 sec. 8.4 sec sec. 199 sec. 13 min. 1 hr. 4 hrs sec sec sec sec sec sec sec. Tornado Z: Average inefficiency = Both codes: Stretch factor = 2

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

16 Cyclic Interleaving: Drawbacks The Coupon Collector s Problem Waiting for packets from the last blocks: T B blocks More blocks: faster decoding, larger inefficiency

17 Scalability over File Size Decoding Inefficiency Decoding Inefficiency, 500 Receivers, p = 0.1 Interleaved, T = 20, Max. Interleaved, T = 20, Avg. Interleaved, T = 50, Max. Interleaved, T = 50, Avg. Tornado Z, Max. Tornado Z, Avg File Size, KB

18 Scalability over Receivers Decoding Inefficiency on a 1MB File, p = 0.1 Decoding Inefficiency Interleaved, T = 20 Interleaved, T = 50 Tornado Z 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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