Scheduling P2P Multimedia Streams: Can We Achieve Performance and Robustness? Luca Abeni, Csaba Kiraly, Renato Lo Cigno DISI – University of Trento, Italy

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

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

IMSAA 2009, Bangalore, 9-11 December 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 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? BA w p  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

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

IMSAA 2009, Bangalore, 9-11 December Bandwidth-Aware ELp Algorithm 2 nd approach: weighted combination  Instead of minimizing L(P j, t) the ID of the latest chunk of neighbour node P j  Consider also Expected arrival of the chunk to P j,  though the bandwidth of the sender s(P i ) Redistribution potential of P j  through the bandwidth of the target peer s(P j ).  Maximize: t − L(P j, t) + B w (s(P j )/s(P i )) Where BW is a weight assigned to the upload bandwidth

IMSAA 2009, Bangalore, 9-11 December Algorithms Comparison We use the P2PTVSim simulator  Open source, event-driven, chunk level simulation  available at 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 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 Bandwidth Distribution Scenarios contd. Uniformly distributed scenario  Peer bandwidth taken from a uniform distribution [1-Δ B,1+Δ B ]  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 class scenario 90 th percentile as a function of heterogeneity neighbourhood size 20 playout delay peers 2000 chunks. Uniform scenario

IMSAA 2009, Bangalore, 9-11 December 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 Free-riders What if some users don’t (or can’t) contribute?  Non BA algorithms (even EL BA p) 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 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