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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op

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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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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

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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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