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

CignoDISI – University of Trento, Italy

[email protected]

IMSAA 2009, Bangalore, 9-11 December 2009 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


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


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


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


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


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!


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


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!


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.


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


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?


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


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 weight assigned to the upload bandwidth


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

http://www.napa-wine.eu/


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]

0

1

0.5 1 1.5 2

h=0

0

1

0.5 1 1.5 2

h=0.5


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

0

1

0.5 1 1.5 2

ΔB=0

0

1

1

ΔB=0.3

0

1

0 0.5 1 1.5 2

r=0

0

1

0 0.5 1 1.5 2

r=0.33


3-class scenario

90th percentile as a function of heterogeneity

neighbourhood size 20

playout delay 50 600 peers 2000 chunks.

Uniform scenario


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.


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.


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

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

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