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Digital Fountains

Digital Fountains

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Digital Fountains

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  1. Digital Fountains -Anand Patwardhan Main Ideas : Distribution of bulk data Reliable multicast, broadcast Ideal digital fountain Erasure codes RMDP Benefits and applicability CSE 581, Winter 2002 Instructor : Wu-chang Feng A. Patwardhan, CSE 581

  2. Paper group • “A Digital Fountain approach to reliable distribution of bulk data”, J. Byers, M. Luby et al. ( Feb. ’98) • “Accessing multiple mirror sites in parallel: Using Tornado codes to speed up downloads”, J. Byers, M. Luby, M. Mitzenmacher • “RMDP: An FEC-based Reliable Multicast Protocol for Wireless Environments”, L. Rizzio, L. Vicisano (April ’98) A. Patwardhan, CSE 581

  3. Reliable distribution of Bulk Data • Multicasting with feedback • Reliability : ARQ - Some solutions use NACK suppression , local recovery – overhead • Scalability – complexity in maintaining group hierarchy • Feedback channel required • Unicasting • Reliability : uses ARQ – Automatic retransmission request • Scalability - Suffers from “Feedback implosion at source” • Efficiency – “repair” packets do not benefit everyone • Requires a feedback channel A. Patwardhan, CSE 581

  4. An Ideal “Digital Fountain” • A Server serving a universe of clients • Source data = k packets is encoded to n = c.k packets (c>1) • Server carousels through a stream of n packets (uses multicast ) • Clients drink their fill • Clients quenched by any k subset of the n packets, then disconnect • Clients can “drink” at anytime (asynchronous) A. Patwardhan, CSE 581

  5. Erasure Codes • Also known as Forward Error-Correcting codes (FEC) • Most commonly used : Reed-Solomon erasure codes • k = source data packets • encoded n = k + l = c.k • c = stretch factor • l = redundant packets • Encoding/decoding complexity increases proportional to k*l*(packetsize) A. Patwardhan, CSE 581

  6. Tornado Codes • Much simpler than Reed-Solomon • Use XOR only • Encoding/decoding uses random bipartite graphs • n = k+l, but slightly more than k packets have to be received at the receiver for decoding ( reception inefficiency) • Fast encoding/decoding at the price of reception inefficiency ( usually around 5%) • Encoding/Decoding complexity proportional to (k+l)ln(1/e)*(packetsize) A. Patwardhan, CSE 581

  7. Comparison of encoding/decoding times A. Patwardhan, CSE 581

  8. Reception overhead 90% clients were done at 0.06 For Tornado A A. Patwardhan, CSE 581

  9. Comparison of reception efficiency for codes with comparable decoding time p = probability of loss A. Patwardhan, CSE 581 Interleaved = data broken into segments and then encoded using Reed-Solomon codes, k = no of segments

  10. Comparison of reception efficiency for codes with increasing filesize A. Patwardhan, CSE 581

  11. Simple Mirroring • User has to pick a single site • Access intervals often overlap • Many to many distribution not possible A. Patwardhan, CSE 581

  12. Parallel download from multiple mirror sites • Digital fountain mirrors • Scalable, efficient, reliable, tolerant and on-demand • Client collects packets from multiple senders until “quenched” • “Out-of-step” senders and increased stretch factor (n = c.k) minimize duplicate packets • All available bandwidth utilized to speed up download (concept of “disjoint bottlenecks”) • Effectively “Many-to-many” distribution A. Patwardhan, CSE 581

  13. Reception inefficiency, Speedup & stretch Duplicates decrease Speedup Increases A. Patwardhan, CSE 581

  14. RMDP protocol • Reliable Multicast data Distribution Protocol • Similar to approach of Digital fountains • Uses Reed-Solomon, with limited ARQ • Authors contend that computational complexity of Reed-Solomon better than resource expensive Tornado codes in the longer term ( CPUs will improve...) • Optimal values of n,k,l used, claim : expensive but adequate • Feedback suppression, using rules for timeouts A. Patwardhan, CSE 581

  15. Benefits Reliability with little or no feedback Highly scalable Clients can join asynchronously Ideal for wireless, satellite, cable transmissions with little or non-existent feedback channel Performance does not decrease with increase in receivers, due to correlated losses Resilient to bursty losses Drawbacks Zero feedback at the cost of bandwidth, buffers and time Encoding, decoding overhead Increased buffers Reception inefficieny Data available only after a minimum set of distinct packets arrive Does not consider CC Reliable multicast using erasure codes A. Patwardhan, CSE 581

  16. Summary • Reliability achieved by “carouseling” packets encoded using erasure codes • A single packet from a block is potentially useful for a large subset of the receivers • Both RMDP and the Digital fountain approach, use multicasting and erasure codes • Tornado codes simpler to decode/encode but memory requirement is non-deterministic (shown to fare better than RS) • Reed-Solomon codes have fixed memory requirement, but computationally very expensive. A. Patwardhan, CSE 581

  17. Questions … A. Patwardhan, CSE 581