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Epidemic Dissemination & Efficient Broadcasting
in Peer-to-Peer Systems
Laurent Massoulié
Thomson, Paris Research Lab
Based on joint work with: Bruce Hajek, Sujay Sanghavi,
Andy Twigg, Christos Gkantsidis and Pablo Rodriguez
2
Context
P2P systems for live streaming & Video-on-Demand– PPLive, Sopcast, TVUPlay, Joost, Kontiki…
Internet hosts form overlay network– Data exchanges between overlay neighbours
– Aim: real time playback at all receivers
Soon the main channel for multimedia diffusion?
3
Diffusion of Code Red Virus
4
Diffusion of Code Red Virus
Logistic curve(Verhulst 1838, Lotka 1925,…)
Exponential growth
Optimal global infection time:logarithmic in population size
5
Epidemics for live streaming diffusion
1 2 43
Data packets
1 2
2
Mechanism specification: selection rule for• target node• packet to transmit
Epidemics (one per packet) competing for resources
6
Problem statement
Currently deployed systems rely on epidemic approach
Appeal of simple & decentralised schemes – Large user populations (103 – 106)
– High churn (nodes join and leave)
“Cost of decentralisation?
i.e., can epidemics make efficient use of communication resources?
Metrics: rate and delay
7
Outline
Delay-optimal schemes
[S. Sanghavi, B. Hajek, LM]
Rate-optimal schemes
[LM, C. Gkantsidis, P. Rodriguez and A. Twigg]
Outlook
8
The access constraint scenario
…
Scarce resource: access capacity
Models DSL / Cable uplink bandwidth limitations
Normalised: 1 packet / second
Bounds on optimal performance
•Throughput = N / (N-1) 1 (pkt / second)
•Delay = log2(N) where N: number of nodes
9
Challenge
Naïve approach Random target First useful packet
1 2 4 5 7 8
1 2 4
Sender’s packets
Receiver’s packets
3
1st useful packet
Fraction of nodes reached
Time
12
3
0.01
0.02
04020
Tension between timeliness of delivery and diversity
10
The “random target / latest packet” policy
1 2 4 5 7 8
? ?
Sender’s packets
Receiver’s packets
Latest packet
??????
Fraction of nodes reached
Time
11
Diffusion at rate 63% of optimal and with optimal delay feasible
(Do source coding at source over consecutive data windows)
The “random target / latest packet” policy
Main result:
Each node receives each packet w.p. 1-1/e 63% with optimal delay ( less than log2(N) ), Independently for distinct packets.
12
t
Proof idea
1 1
11 1
eNN
NN
time
Fraction of nodes
t+1
Nodes that have pkt with label t
Nodes that have pkt with label t+1
Number of transmission attempts for packet t: N area between curves = N
1
Number of nodes receiving t:
Same dynamics as single epidemic diffusiontranslated logistic curve
sfesfsfsf
Nf
1 11
,1
0
13
Outline
Delay-optimal schemes
[S. Sanghavi, B. Hajek, LM]
Rate-optimal schemes
[LM, C. Gkantsidis, P. Rodriguez and A. Twigg]
Outlook
14
Access constraints scenario
Network assumptions:
– access capacities, ci
– Everyone can send to everyone (complete communication graph)
Statistical assumptions: – source creates fresh packets at instants of Poisson process with rate λ
– Packet transmission time from node i: Exponential r.v. with mean 1/ci
Optimal broadcast rate:
i
is cN
c1
1 , min*
15
The “Most deprived neighbour / random useful packet” policy
1 2 4 5 7 8
Sender’s packets
1 5 7 8 1 4
Potential receiver 1 Potential receiver 2
5
Source policy: sends “fresh” packets if any(fresh = not sent yet to anyone)
16
Main result
Provided λ < λ*, Markov process describing system state is ergodic.
Hence all packets are received at all nodes after time bounded in probability
Proof: identifies “workload” as Lyapunov function for fluid dynamics of Markov process
Open questions: Magnitude of delays (simulations suggest logarithmic) Extension to general, not complete graphs
17
Extension to limited neighborhoods
Each node maintains shortlist of neighboursSends to most deprived from neighbour setPeriodically adds randomly chosen neighour, and
dumps least deprivedNeighbourhood size stays fixed
Ergodicity result still holds: fluid dynamics unchanged
Q: impact of neighborhood size?
18
Network constraints
•Graph connecting nodes •Capacities assigned to edges
Achievable broadcast rate [Edmonds, 73]:Equals maximal number of edge-disjoint spanning trees that can be packed in graphCoincides with minimal max-flow ( = min-cut) between source and arbitrary receiver
19
Based on local informations
No explicit construction of spanning trees
Random useful packet selection and Edmonds’ theorem
1 4
51 2 4 5 7 8
Main result:
When injection rate λ strictly feasible,
Markov process is ergodic
?
??
?
?
?
??
?
20
Proof idea
s
1 2
3Original network
Variables xA: Number of packets present exactly at nodes in set A
•Fluid Renormalisation: The xA obey deterministic dynamics
s,1
s
s,1,2,3
s,2
s,1,3 s,2,3Induced network
s,1,2
λ
λ ?
•Convergence to zero of fluid trajectories:
shown by using Lyapunov function VAxxL AA : sup)(
21
Comments
Provides “analytical” proof of Edmond’s theoremDelays?
22
Conclusions
Epidemic diffusion – Straightforward implementation
– Efficient use of bandwidth resources
Random & local decisions lead to global optimum
23
Outlook
Open problems– Schemes both delay- and rate- optimal?
– Concurrent stream diffusions?
– Stability proofs without the Lyapunov function?