Transcript Cheese Factory

UNIVERSITY OF JYVÄSKYLÄ
Resource Discovery in P2P Networks
Using Evolutionary Neural Networks
Presentation for International Conference on Advances in
Intelligent Systems – Theory and Applications (AISTA 2004)
15.11.2004
Mikko Vapa, researcher student
Agora Center
http://tisu.it.jyu.fi/cheesefactory
With co-authors Niko Kotilainen, Annemari Auvinen,
Heikki Kainulainen and Jarkko Vuori
UNIVERSITY OF JYVÄSKYLÄ
Peer-to-Peer Networks
• Peer-to-Peer networks (P2P) are formed by Transmission
Control Protocol (TCP) connections between workstations
• Workstations denoted as nodes can share their resources for
example files (
) or computing power
TCP
Node 2
Node 1
TCP
TCP
TCP
TCP
Node 4
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Node 3
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Resource Discovery Problem
• In peer-to-peer resource discovery problem any node in the
network can query resources from other nodes
Node1: Where is
?
Node 2
Node 1
Node 4
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Node 3
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A Simple Solution for the Problem
• Gnutella P2P network for example uses Breadth-First Search
(BFS) flooding algorithm which sends query to all neighbors
• Problems: all resources in the network can be found, but
network gets congested and there are lots of useless packets
Node 2: I have it!
Node 4: Node 4 has it too!
Reply
Node 1: Where is
?
Query
Node 2
Query
Node 1
Query
Query
Query
Reply
Node 4: I have it!
Query
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Node 3
Node 4
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Our solution: NeuroSearch
• NeuroSearch resource discovery algorithm uses neural networks
and evolution to adapt its behavior to given environment
– neural network for deciding whether to pass the query further
down the connection or not
– evolution for breeding and finding out the best neural
network in a large class of local search algorithms
• To authors’ knowledge this is the first time when neural networks
are being applied to resource discovery problem
Forward
the query
Neighbor Node
Query
Forward
the query
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Neighbor Node
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NeuroSearch’s Inputs
• The internal structure of NeuroSearch algorithm
Threshold
Tanh
Tanh
• Multiple layers enable the algorithm to express non-linear
behavior
• With enough neurons the algorithm can universally approximate
any decision function
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NeuroSearch’s Training Program
• The neural network weights define how neural network behaves
so they must be adjusted to right values
• This is done using iterative optimization process based on
evolution and Gaussian mutation
Define the
P2P network
conditions
Define the fitness
requirements
for the algorithm
Create candidate
algorithms
randomly
Select the best
ones for next
generation
Finally select the
best algorithm for
these conditions
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Iterate
thousands
of
generations
Breed a new
population
Compare the best
one against
Breadth-First
Search
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Well How Good Is The Algorithm?
• We defined a peer-to-peer network scenario where:
– 100 nodes form a power-law distributed P2P
network having few hubs and lots of
low-connectivity nodes
– Resources are distributed based on the number of
connections the node has meaning that high-connectivity
nodes are more likely to answer to the queries
– Topology is static so the nodes are not moving
• Then we defined a fitness function for the algorithm stating that:
– An algorithm that stops is always better than algorithm that
does not
– The algorithm should locate half of the available resources for
each query
– The algorithm should use as minimal number of packets as
possible
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Well How Good Is The Algorithm?
•
•
After two weeks we were ready to compare NeuroSearch’s
invention against Breadth-First Search in 100-query test scenario
The measurements indicate that the optimization process had
developed an algorithm that:
– finds half of the resources in the network with high probability
– is more efficient than BFS with maximum number of three hops
(BFS-3) and as efficient as BFS-2 while still locating the required
50% of resources
– has stable performance regardless of where the querier is located
Algorithm
Packets
Resources
Resources/Packets
(Efficiency)
BFS-2
3000
619 (37,1%)
0.2063
BFS-3
12202
1295 (66,7%)
0.1061
NeuroSearch
4719
975 (53,2%)
0.2066
 Conclusion is that the approach is feasible, but not yet optimal
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Evolution Of Neural Networks
The best neural
network of 85,736th
generation was
selected for testing
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Performance of NeuroSearch – Hit Rate
NeuroSearch slightly
misses the target of 50%
resources in 8 queries
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Performance of NeuroSearch - Resources
BFS locates more
resources when query
starts from central nodes
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Performance of NeuroSearch - Packets
NeuroSearch is stable and
the performance does not
depend on where the
query is started
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Typical query pattern of NeuroSearch
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The maximum
number of hops is 5
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Future Work
• Now the first version of NeuroSearch is ready and analyzed
• The future work of NeuroSearch includes:
– Analysis of the effects of varying neural network’s structure
• New input types to feed NeuroSearch with more
information
• Adjusting the number of neurons to allow NeuroSearch to
make wiser decisions
– Studying the scalability factors affecting NeuroSearch when
the P2P network size grows
– Developing an optimal resource discovery algorithm using
global knowledge to be able to measure the best efficiency
resource discovery algorithm can achieve
– Speeding up the optimization process by parallelizing
evolutionary algorithm using distributed computing
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Thank You!
Any questions?