ADDRESSING CHURN IN A PEER-TO-PEER EVOLUTIONARY ALGORITHM
J.L.J. Laredo, P.A. Castillo, A.M. Mora, C. Fernandes,
J.J. Merelo
Dept. of Architecture and Computer Technology
University of Granada

Scope
P2P systems are large networks of volatile
resources, the dynamics are known as
CHURN.
1. P2P EA viable?
2. Is it a P2P EA FAULT TOLERANT under
CHURN ?

Outline
Introduction
P2P EAs Issues
Experimental Setup
Results
Conclusions

Outline
 P2P EA
Introduction  Evolvable Agent Model
P2P EAs Issues
Experimental Setup
Results
Conclusions

Outline
Introduction
 Decentralization
P2P EAs Issues  Scalability
 Fault Tolerance
Experimental Setup
Results
Conclusions

Outline
Introduction
P2P EAs Issues
 Modelling Churn
Experimental Setup  MMDP
 Some Considerations
Results
Conclusions

Outline
Introduction
P2P EAs Issues
Experimental Setup
Results
Conclusions

Outline
Introduction
P2P EAs Issues
Experimental Setup
Results
Conclusions

Introduction
• Volunteer Computing
Overlay Network
• Convergence P2P GRID
• Application Level Networks
•Dynamic neighborhood
(i.e. Small World)
Physical Network P2P EA
Evolvable Agent Model

Introduction
Evolvable Agent
St  Initialize
DO
Sols  Selection
St +1  Recombination(Sols,Pc )
Evaluation (St +1)
If St +1 better than St
St  St + 1
P2P EA
Evolvable Agent Model

P2P EAs Issues
Decentralization
Scalability
Fault Tolerance

P2P EAs Issues
Decentralization
Scalability
Fault Tolerance

P2P EAs Issues
• Cuadratic number of edges • Logarithmic number of edges
• High clustering coefficient • High clustering coefficient
Decentralization
Scalability
Fault Tolerance

P2P EAs Issues
T0 T1 T2
T5 T4 T3
Peer Session length Inter-arrival
1 2 Decentralization
Scalability
2 1
Fault Tolerance

P2P EAs Issues
T0 T1 T2
T5 T4 T3
Peer Session length Inter-arrival
1 2 Decentralization
Scalability
2 1
Fault Tolerance

Experimental Setup
CHURN
•Stutzbach and Rejaie
•Weibull Distribution
1
s
X ( ln(U ))
• s = 0.40
• λ= 1, 5, 10, 50
Peer Session length Inter-arrival
1 2 Modelling Churn
MMDP
2 1
Some Considerations

Experimental Setup
Massively Multimodal Deceptive Problem
Instances k = 2, 4, 8, 16, 32, 64
GA EvAg Newscast
Population Bisection
Size 0.98 of SR
1 0 1 0 0 0 1 1 1 1 0 1 1 0 1 1 0 0
1, 2… k
Modelling Churn
MMDP
Some Considerations

Experimental Setup
EA Population Size Є (40, 400)
Wrong assumption!!!
• Population Size depends on the problem instance complexity
• Larger Instances require larger population sizes
• Practitioners tackle large instances with few individuals due to a
lack in resources
• We have many resources in P2P systems
Modelling Churn
MMDP
Some Considerations

Results

Results

Results
k=64
λ=1
0.4% of the
initial population

Conclusions
Agent-based approach for dEA on P2P
Algorithmically viable, 0.98 SR
Massively scalable
Resilient to churn

THANK YOU!