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Detection of Emergent Behaviour in Active Networks

This presentation explores the concept of emergence in active networks and presents a detection technique based on self-similarity. The modelling strategy and simulation environment are discussed, along with interesting results and conclusions.

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Detection of Emergent Behaviour in Active Networks

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  1. Detection of Emergent Behaviour in Active Networks By Prof. David Parish and Mr. Shirantha de Silva HSN Group Loughborough University

  2. Presentation Overview • Brief definition of “Emergence” • Introduction to the system – an Active Network • Modelling Strategy and Simulation environment • Detection technique – “Self-similarity” • An interesting result • Conclusions

  3. What is Emergence? • “A set of individual interactions that result in a coherent whole, which cannot be deduced from examining the properties of the individual.” • An example….

  4. A set of system attributes that could lead to Emergence • Distributed processing architecture • In-built intelligence and self-awareness • Local network awareness • Lack of central management/control • Application level organisation • Adaptation and evolution • Memory • Limited resources and competition

  5. Active Network – a Systems’ view • More complex than traditional networks. • Packets have the potential to change the state and behaviour of nodes. • Unexplained phenomena can emerge due to the ‘intelligence’ of nodes.

  6. Modelling Strategy • High level abstract model • An application level analysis of Active Networks • Is not tied in with any Active Network scheme • Simplification of model and simulation - low resolution • Simulation output is focused on the detection of ‘Emergence’

  7. Modelling Strategy • Primitive Functional Components • Data Replication • Data Fusion • Data Generation • Data Transformation • Global State Maintenance • Network Control Processing

  8. Simulation Environment – PETRI NETS Circles – “Places” – network state and information storage Squares – “Transitions” – command execution blocks Dots – “Tokens” – information movement

  9. Simulation Environment – PETRI NETS • A token will correspond to an Active Packet • The Colour Petri-Net scheme can then differentiate between types of Active packet • An Active process will correspond to a combination of Transitions and Places. • Places represent network state and storage of resources.

  10. Simulation Environment – PETRI NETS

  11. Detection technique • We propose that Resource usage fluctuations of Active Nodes are a key indicator of Emergence! • Detection technique will focus on finding patterns in output data (resource usage statistics of the 25 Active Nodes) • Patterns in the data may indicate the presence of Emergence • Self-similarity – a pattern appearing in a data series in various time resolutions

  12. Detection technique – “Self-similarity” • Self-similarity measured through the Hurst parameter (H) • 0.5 (random variation) <= H <= 1 (completely self-similar) • Calculate the Hurst parameter for each Active Node - resource usage variation over simulation time period • Used the Rescaled-range Statistic (R/S) technique to calculate H

  13. An Interesting Result • Was discovered when the network was forced into a potentially uncontrolled state • An Active packet was injected, which replicated itself at every Active node it encountered that had adequate resources • The result of this type of replication is fluctuations in resource usage. • Resource usage fluctuations are self-similar!!!

  14. An Interesting Result

  15. An Interesting Result • 40% of the nodes show an above 0.9 Hurst value • Active replication packet resource usage requirement is 20% of total • Initial direction of replicating packet is from node A5 to E1 • Conclusion: Emergent behaviour present!

  16. Another Interesting Result

  17. Another Interesting Result • 0% of the nodes show an above 0.9 Hurst value • Active replication packets’ resource usage requirement is 85% of total, 79% of total, 60% of total and 27% of total • Initial directions of replicating packets is from node B1 to A2, from node E4 to A4, from node E5 to A1 and from node D5 to C5, respectively • Conclusion: no Emergence Behaviour present!

  18. Conclusion • Two factors have been identified to affect the Replicating Active Application’s / Packet’s ability to exhibit self-similarity: • The ‘general’ path of the original Active replicating packet • The amount of resources an Active replicating packet consumes within the node and the amount of time for which it reserves these resources

  19. And finally… • Detecting Emergence is complicated and tricky • Different techniques for different types of Emergence • Focusing on system resource usage is a good starting point • High-level abstract modelling is a good idea • “One man’s Emergence is another’s obviousness” Thank you!

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