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Scheduling P2P Multimedia Streams: Can We Achieve Performance and Robustness?

Scheduling P2P Multimedia Streams: Can We Achieve Performance and Robustness?. Luca Abeni, Csaba Kiraly , Renato Lo Cigno DISI – University of Trento, Italy kiraly@disi.unitn.it. P2P Multimedia Streaming. P2P is cool, but why streaming? Think of out-of-country TV broadcasting

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Scheduling P2P Multimedia Streams: Can We Achieve Performance and Robustness?

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  1. Scheduling P2P Multimedia Streams: Can We Achieve Performance and Robustness? Luca Abeni, Csaba Kiraly, Renato Lo Cigno DISI – University of Trento, Italy kiraly@disi.unitn.it

  2. P2P Multimedia Streaming • P2P is cool, but why streaming? • Think of out-of-country TV broadcasting • easier to get Internet connection than a satellite dish • Think of the cost of starting a new TV channel • traditional TV broadcasting vs. client-server vs. P2P • P2P-TV could become one of the dominant multimedia applications on the Internet • Some systems already deployed: PPLive, TVAnts, CoolStreaming, … with hundreds of channels already available IMSAA 2009, Bangalore, 9-11 December 2009

  3. P2P Multimedia Streaming contd. • P2P-TV is resource-hungry • previously unseen traffic volumes to/from the users • 1+ mbit/s sustained download • Even higher upload (if available) • P2P-TV is challenging to design • large peer count with heterogeneous networking resources • This is not VoD, potentially millions of users watching the same live channel • tight delay constraints • This is not file sharing, delay is the design objective IMSAA 2009, Bangalore, 9-11 December 2009

  4. Achieve Performance & Robustness • Several design challenges • organizing and maintaining the P2P overlay • scheduling information transmission between peers • etc. • In this work, we • concentrate on scheduling for chunk-based P2P streaming • study different combinations of peer and chunk selection strategies • propose a new peer selection strategy that achieves both performance and robustness IMSAA 2009, Bangalore, 9-11 December 2009

  5. Outline of Talk • P2P streaming systems, definitions • The scheduling problem • Chunks selection strategies (RUc, LUc, DLc) • Peers selection strategies (RUp, MDp, ELp, BAWp) • The optimal ones … are these robust? • Bandwidth-Aware ELp Algorithm (BAELp) • Algorithms Comparison IMSAA 2009, Bangalore, 9-11 December 2009

  6. P2P Streaming Systems • A source generates encoded audio/video • This media stream is divided into chunks • Various peers receive the encoded media and contribute to the diffusion, by forwarding received chunks to other peers • The system is unstructured • No fixed distribution tree • Each peer is connected to a small subset of the other peers (neighbourhood) • Chunks are exchanged among neighbour peers IMSAA 2009, Bangalore, 9-11 December 2009

  7. The Scheduling Problem • Each peer • Receives chunks from the other peers • Redistributes chunks to neighbour peers • Scheduling decision at the sender peer • Which chunk to send? (chunk selection) • To which neighbour send a chunk?(peer selection) • 2 variants • Chunk first selection (XXc/XXp) • Peer first selection (XXp/XXc) We concentrate on chunk first selection! IMSAA 2009, Bangalore, 9-11 December 2009

  8. Chunk Selection • Random Useful (RUc): • select among the chunks useful to at least one neighbour with uniform random choice • Rationale: • If there is enough bandwidth, sooner or later useful chunks get there • easy to implement, widely used as baseline performance • Latest Useful (LUc): • Rationale: spread new chunks as fast as possible • Shown to be fragile: older chunks can be "overtaken“ by newer ones, stopping their diffusion • This fragility increases as neighbourhood size is reduced IMSAA 2009, Bangalore, 9-11 December 2009

  9. Chunk Selection contd. • Deadline-based scheduler (DLc): • Rationale: embed meta-information in the chunk instance • Each copy of each chunk is associated a scheduling deadline, initialized to the chunk generation time • Deadline of the chunk instance in the sender peer is postponed each time chunk is sent • The useful chunk with the earliest deadline is selected • shown to overcome problems of LUc • No “overtaking” effect • good performance with small neighbourhood size We will use DLc in this paper! IMSAA 2009, Bangalore, 9-11 December 2009

  10. Peer Selection • Random Useful Peer (RUp): • Uniform random choice among the peers that need the given chunk • Bandwidth Aware Peer scheduler (BAWp): • Rationale: peers with high upload bandwidth has high redistribution potential • randomly selects a target (as in RUp); the probability of selecting Pj is proportional to its output bitrate. IMSAA 2009, Bangalore, 9-11 December 2009

  11. Peer Selection contd. Earliest-Latest Peer (ELp): • Rationale: key to fast diffusion is to choose a peer that can re-distribute the chunk • Check the latest chunk owned by each peer • And select as a target the peer with the earliest latest chunk IMSAA 2009, Bangalore, 9-11 December 2009

  12. The Optimal Ones • ELp • shown to be optimal in idealized conditions • Homogeneous peers: for each peers • upload bandwidth = stream bandwidth • What happens in heterogeneous networks? • BAwp • Shown to achieve good performance in largely heterogeneous networks • But it falls back to RUp for homogeneous networks! Are any of these robust to various network scenarios? IMSAA 2009, Bangalore, 9-11 December 2009

  13. IMSAA 2009, Bangalore, 9-11 December 2009

  14. Bandwidth-Aware ELp Algorithm • Goal: blend the best properties of bandwidth aware heuristics with ELp optimality • 1st approach: hierarchical scheduling • ELBAp: use EL first. If there is a tie, apply BA among winners • BAELp: BA first, EL after IMSAA 2009, Bangalore, 9-11 December 2009

  15. Bandwidth-Aware ELp Algorithm • 2nd approach: weighted combination • Instead of minimizing L(Pj , t) • the ID of the latest chunk of neighbour node Pj • Consider also • Expected arrival of the chunk to Pj, • though the bandwidth of the sender s(Pi) • Redistribution potential of Pj • through the bandwidth of the target peer s(Pj). • Maximize: t − L(Pj , t) + Bw(s(Pj)/s(Pi)) • Where BW is a weightassigned to the upload bandwidth IMSAA 2009, Bangalore, 9-11 December 2009

  16. Algorithms Comparison • We use the P2PTVSim simulator • Open source, event-driven, chunk level simulation • available at http://www.napa-wine.eu • Critical resource is the overall upload bandwidth in the system • We model the network as upload bandwidth limits at the peer’s access link • Download bandwidth assumed to be unlimited • We study three bandwidth distribution scenarios • Each scenarion has a [0..1] heterogeneity parameter IMSAA 2009, Bangalore, 9-11 December 2009

  17. Bandwidth Distribution Scenarios • We fix the average upload bandwidth at 1 (the source rate) • The 3-class scenario • ADSL like bandwidth distribution • High-, mid- and low-bandwidth classes • h: heterogeneity factor [0..1] IMSAA 2009, Bangalore, 9-11 December 2009

  18. Bandwidth Distribution Scenarios contd. • Uniformly distributed scenario • Peer bandwidth taken from a uniform distribution [1-ΔB,1+ΔB] • To avoid artifacts due to class-based distributions • Free-rider scenario • With peers that only leach, do not contribute IMSAA 2009, Bangalore, 9-11 December 2009

  19. 3-class scenario • 90th percentile as a function of heterogeneity • neighbourhood size 20 • playout delay 50 • 600 peers • 2000 chunks. • Uniform scenario IMSAA 2009, Bangalore, 9-11 December 2009

  20. Excess resources • What if excess upload bandwidth is available? • Performance improves and differences diminish • BAELp uses bandwidth more efficiently neighbourhood size 20; playout delay 50; Uniform with B = 0.8;N = 1000 peers, Mc = 2000 chunks. IMSAA 2009, Bangalore, 9-11 December 2009

  21. Free-riders • What if some users don’t (or can’t) contribute? • Non BA algorithms (even ELBAp) fail at 15-20% of free-riders • BAELp remains top performer neighbourhood size 100; playout delay 50: F90 versus the fraction of the free riders. B = 1, N = 1000 peers, Mc = 2000 chunks. IMSAA 2009, Bangalore, 9-11 December 2009

  22. Summary and Future Work • Summary • We have compared several scheduling algorithms from previous literature, showing their weaknesses • Designed the BAELp algorithm, which outperforms other algorithms in a large number of scenarios • Our future work • Formal analysis of BAELp, and its weight parameter • Improve simulations with video trace driven chunk generation and evaluation of the received video quality IMSAA 2009, Bangalore, 9-11 December 2009

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