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
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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
Introduction
P2P EAs Issues
Experimental Setup
Results
Conclusions
P2P EA
Evolvable Agent Model
Outline
Introduction
P2P EAs Issues
Experimental Setup
Results
Conclusions
Decentralization
Scalability
Fault Tolerance
Outline
Outline
Introduction
P2P EAs Issues
Experimental Setup
Results
Conclusions
Modelling Churn
MMDP
Some Considerations
Introduction
P2P EAs Issues
Experimental Setup
Results
Conclusions
Outline
Introduction
P2P EAs Issues
Experimental Setup
Results
Conclusions
Outline
Overlay Network
Physical Network
Introduction
P2P EA
Evolvable Agent Model
• Volunteer Computing
• Convergence P2P GRID
• Application Level Networks
•Dynamic neighborhood
(i.e. Small World)
Evolvable Agent
St Initialize
DO
Sols Selection
St +1 Recombination(Sols,Pc )
Evaluation (St +1)
If St +1 better than St
St St + 1
Introduction
P2P EA
Evolvable Agent Model
P2P EAs Issues
Decentralization
Scalability
Fault Tolerance
P2P EAs Issues
Decentralization
Scalability
Fault Tolerance
P2P EAs Issues
Decentralization
Scalability
Fault Tolerance
• Cuadratic number of edges
• High clustering coefficient
• Logarithmic number of edges
• High clustering coefficient
Peer Session length Inter-arrival
1 2
2 1
Decentralization
Scalability
Fault Tolerance
P2P EAs Issues
T0 T1 T2
T3T4T5
Peer Session length Inter-arrival
1 2
2 1
Decentralization
Scalability
Fault Tolerance
P2P EAs Issues
T0 T1 T2
T3T4T5
Modelling Churn
MMDP
Some Considerations
Experimental Setup
Peer Session length Inter-arrival
1 2
2 1
•Stutzbach and Rejaie
•Weibull Distribution
• s = 0.40
• λ= 1, 5, 10, 50
sUX1
))ln((
CHURN
Modelling Churn
MMDP
Some Considerations
Experimental Setup
1 0 1 0 0 0 1 1 1 1 0 1 1 0 1 1 0 0
1, 2… k
Massively Multimodal Deceptive Problem
Instances k = 2, 4, 8, 16, 32, 64
GA EvAg Newscast
Population
Size
Bisection0.98 of SR
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!
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