5 saso2012-presentation
TRANSCRIPT
Motivation Scenario Algorithm Evaluation
A Cognitive-Inspired Model for Self-OrganizingNetworks
ASENSIS 2012
Daniel Borkmann0 Andrea Guazzini12 Emanuele Massaro3
Stefan Rudolph4
0Communication Systems Group, ETH Zurich, Switzerland1Institute for Informatics and Telematics, National Research Council, Pisa, Italy
2Department of Psychology, University of Florence, Italy3Department of Informatics and Systems, University of Florence, Italy
4Organic Computing Group, University of Augsburg, Germany
10th September, 2012
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Motivation Scenario Algorithm Evaluation
Large Scale Network
Source: http://de.wikipedia.org/w/index.php?title=Internet&oldid=107566536
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Motivation Scenario Algorithm Evaluation
Motivation
Large Scale Networks emergeInternetPervasive ComputingOften used: Overlay networks
Problems of overlay networksStructured: Hard without global informationUnstructured: No optimization of network structure
IdeaSelf-optimization of an overlay networkThrough a cognitive-inspired model
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Motivation Scenario Algorithm Evaluation
Scenario
Connected network of n nodesStatic, nodes don’t disappear or appearEach holdes one item (e.g. a service or data)Each wants to retrieve items with respect to its energyEach has a limited number of links from 1 . . .mEach node can change its links
Optimization problems: change links in order toRetrieve all items with the minimum number of hopsMaximize the number of items with a fixed amount of hops
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Motivation Scenario Algorithm Evaluation
Cognitive-Inspired Hub DetectionDiffusion and Competitive Interaction
At startA is the adjacency matrixEvery node i has a state vector Si (short term memory)S(k)
i is the probability that node i belongs to community kEvery node belongs to its own community
Update of the state vectors
S(t + 12 ) = mSik (t)+(1−m)∑j AijSjk (t)
S(t +1) =Sα
ik (t+12 )
∑j Sαij (t+
12 )
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Motivation Scenario Algorithm Evaluation
Cognitive-Inspired Hub DetectionDiffusion and Competitive Interaction
EntropyEi =−∑(Sj · log(Sj))Plateaus show sub-clustersWhen curvature changes sign, save information in temporarymemory box
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Entr
op
y
Time
Shannon entropy of information
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Motivation Scenario Algorithm Evaluation
Cognitive-Inspired Hub DetectionCognitive Dissonance
Cognitive concept found by social psychologistsReduces conflicting cognitionsCreates consistent belief system
Here: Dij :=∑k |Sik−Sjk |
2
Interesting for adaption of α :
If∣∣∣E t−1
i +Dt−1i
Ki
∣∣∣− ∣∣∣E ti +Dt
iKi
∣∣∣< ε for more than τ∗ times
Set αi = 1.5|η(0,σ)|+1
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Motivation Scenario Algorithm Evaluation
Cognitive-Inspired Hub DetectionLong Term Memory
Store potential hubs in the Long Term MemoryFind B1 time positions by sorting with respect to first derivativeSort the remaining vectors with respect to the entropyFind the potential hubs in the state vectors
Use Long Term Buffer of size B2
The last B2 sets of size B1 are stored (bounded rationality)This creates a (B1,B2) matrix
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Motivation Scenario Algorithm Evaluation
Rewiring
With help of this Long Term Memory, we can can create a “hublist" for each node
Rewiring steps:1. Find the weakest X% of the nodes2. Choose Y% of the nodes at random3. Each of these nodes closes a connection to a non-hub4. Each of these nodes opens a new connection to a potential hub
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Motivation Scenario Algorithm Evaluation
Network Example
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Motivation Scenario Algorithm Evaluation
Network Example
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Motivation Scenario Algorithm Evaluation
Network Example
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Motivation Scenario Algorithm Evaluation
Network Example
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Motivation Scenario Algorithm Evaluation
Network Example
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Motivation Scenario Algorithm Evaluation
Numerical SimulationScenarios
1. Maximization of the reachable items of the nodesThe energy (hops) is limitedWeakest nodes: Minimum number of items
2. Minimization of used energyAll item will be reached in every stepWeakest nodes: Maximum number of energy
Randomized AlgorithmFor comparisonDoes not use hub list
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Motivation Scenario Algorithm Evaluation
Numerical SimulationParameters
Number of nodes n
Mean connectivity
Mean extra connectivity
Number of unique items I
Number of items to retrieve Imax
Hub detection: m,α
Rewiring
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Motivation Scenario Algorithm Evaluation
EvaluationResults for maximization of retrieved items
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Mean fi
tness
(I c
urr
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Round
Topology Optimization
Rewiring, cognitive approach Rewiring, randomized approach
Setting: Mean over 50 runs, n = 200, mean_conn= 4, extra_conn= 4, I = 50, Imax = 45, rw_weak= 0.09, rw_rand= 0.03
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Motivation Scenario Algorithm Evaluation
EvaluationResults for the minimization of energy
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Mean e
nerg
y
Round
Topology Optimization
Rewiring, cognitive approach Rewiring, randomzied approach
Setting: Mean over 50 runs, n = 200, mean_conn= 4, extra_conn= 4, I = 50, Imax = 45, rw_weak= 0.09, rw_rand= 0.03
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Motivation Scenario Algorithm Evaluation
Conclusion
ContributionsDevelopment of a cognitive model for community detectionApplication of information for self-optimization of a networkComparison with a randomized algorithm
Future Work(i) Evaluate the algorithm on a wide range of large scale network
topologies(ii) Localize the decision making of a node when to rewire or not(iii) Introduce more dynamics into items and nodes
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