Gradient-based Self-organisation Patterns of Anticipative Adaptation
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Gradient-based Self-organisation Patterns of Anticipative Adaptation

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The self-organisation Gradient pattern is known to be a key spatial data structure to make information local to its source become global knowledge, and to dynamically and adaptively steer agents to ...

The self-organisation Gradient pattern is known to be a key spatial data structure to make information local to its source become global knowledge, and to dynamically and adaptively steer agents to that source even in mobile and faulty environments – e.g. when obstacles unpredictably appear. In this paper we conceive new self-organisation mechanisms built upon this pattern to tackle anticipative adaptation. We ensure that the retrieval of a target of interest proactively reacts to locally-available information about future events, namely, the knowledge about future obstacles (e.g., expected jams or road interruption in a traffic control scenario) is used to emergently compute alternative and faster paths.

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Gradient-based Self-organisation Patterns of Anticipative Adaptation Gradient-based Self-organisation Patterns of Anticipative Adaptation Presentation Transcript

  • Gradient-based Self-organisation Patterns of Anticipative Adaptation Sara Montagna, Danilo Pianini and Mirko Viroli sara.montagna@unibo.it Alma Mater Studiorum—Universit` di Bologna a Cesena a Sixth IEEE International Conference on Self-Adaptive and Self-Organizing Systems Lyon, France; 11th September 2012Montagna, Pianini, Viroli (UNIBO) Anticipative Gradient SASO 2012 1 / 18
  • OutlineGoal and result Start from a catalogue of design patterns for spatial self-organisation Aim at extending the gradient pattern with temporal aspects Design the antipative gradient pattern (“proacting” to known future!) Build it by combination of simpler patterns Qualitative evaluation by simulationMontagna, Pianini, Viroli (UNIBO) Anticipative Gradient SASO 2012 2 / 18
  • Self-organisation Patterns: reusable design elements⇒ Start from the layered catalogue in [FMDMSM+ 12]The gradient pattern case Information about a source node becomes global knowledge Information to reach the source is propagated hop-by-hop Self-heal to changes, but tackling only “present-awareness”Montagna, Pianini, Viroli (UNIBO) Anticipative Gradient SASO 2012 3 / 18
  • When spatial gradient is not enoughMontagna, Pianini, Viroli (UNIBO) Anticipative Gradient SASO 2012 4 / 18
  • When spatial gradient is not enoughMontagna, Pianini, Viroli (UNIBO) Anticipative Gradient SASO 2012 5 / 18
  • When spatial gradient is not enoughMontagna, Pianini, Viroli (UNIBO) Anticipative Gradient SASO 2012 6 / 18
  • When spatial gradient is not enoughMontagna, Pianini, Viroli (UNIBO) Anticipative Gradient SASO 2012 7 / 18
  • When spatial gradient is not enoughMontagna, Pianini, Viroli (UNIBO) Anticipative Gradient SASO 2012 8 / 18
  • When spatial gradient is not enoughMontagna, Pianini, Viroli (UNIBO) Anticipative Gradient SASO 2012 9 / 18
  • From Gradient to Anticipative GradientFrom space to space-time: anticipative gradient The Gradient should deviate now to anticipate known later eventsSome design guidelines Design anticipative gradient by combining more elementary patterns Assume estimated node-to-node average travelling time is availableMontagna, Pianini, Viroli (UNIBO) Anticipative Gradient SASO 2012 10 / 18
  • From Gradient to Anticipative GradientFrom space to space-time: anticipative gradient The Gradient should deviate now to anticipate known later eventsSome design guidelines Design anticipative gradient by combining more elementary patterns Assume estimated node-to-node average travelling time is availableMontagna, Pianini, Viroli (UNIBO) Anticipative Gradient SASO 2012 10 / 18
  • Spatial Structure: Horizon WaveHorizon WaveAdvertises a future event Creates a shrinking crown around the source of the future event points It’s the set of nodes possibly reaching the source during the event Figure : Horizon Wave pattern, with its shrinking dynamics in evidence.Montagna, Pianini, Viroli (UNIBO) Anticipative Gradient SASO 2012 11 / 18
  • Spatial Structure: Gradient ShadowAllows to identify multiple different paths toward the POI Tags the gradient paths passing by some future event area Nodes store (all) the paths transiting/non-transiting across FEs Figure : Shadow spatial structure with overlapping Future EventsMontagna, Pianini, Viroli (UNIBO) Anticipative Gradient SASO 2012 12 / 18
  • Spatial Structure: Future Event WarningFuture Event WarningUsers in this area will reach the event Intersection of Gradient Shadow and Horizon Wave. Figure : Warning spatial structure as intersection of Wave and ShadowMontagna, Pianini, Viroli (UNIBO) Anticipative Gradient SASO 2012 13 / 18
  • Spatial Structure: Anticipative GradientAnticipative GradientChooses the time-shortest path Penalises those paths passing through the event Users travelling those paths must wait for the event to finish Figure : Anticipative Gradient pattern (and “waiting distance” T ).Montagna, Pianini, Viroli (UNIBO) Anticipative Gradient SASO 2012 14 / 18
  • Early evaluation “Implementation” as chemical-like reactions (SAPERE [ZCF+ 11]) Used Alchemist simulator (http://alchemist.apice.unibo.it) 0.7 0.5 0.6 0.4 0.5 0.3 0.4 0.3 0.2 0.2 0.1 0.1 0 0Figure : An Anticipative Gradient case: (Left) estimated distance normalised bythe maximum value; (Right) time-to-destination improvement factor and steeringdirection.Montagna, Pianini, Viroli (UNIBO) Anticipative Gradient SASO 2012 15 / 18
  • Conclusions 1 Added proactive adaptation to the Gradient pattern 2 By identification and composition of simpler patterns Wave, Shadow, Warning, Anticipative GradientFuture works 1 Dealing with a wider set of future events 2 Deep analysis of performance achievements 3 Application of the approach to real scenarios of traffic/crowd routing 4 Prototype implementation in the SAPERE framework [ZCF+ 11];Montagna, Pianini, Viroli (UNIBO) Anticipative Gradient SASO 2012 16 / 18
  • ReferencesReferences I Jose Luis Fernandez-Marquez, Giovanna Di Marzo Serugendo, Sara Montagna, Mirko Viroli, and Josep Lluis Arcos. Description and composition of bio-inspired design patterns: a complete overview. Natural Computing, May 2012. Online First. Franco Zambonelli, Gabriella Castelli, Laura Ferrari, Marco Mamei, Alberto Rosi, Giovanna Di Marzo Serugendo, Matteo Risoldi, Akla-Esso Tchao, Simon Dobson, Graeme Stevenson, Juan Ye, Elena Nardini, Andrea Omicini, Sara Montagna, Mirko Viroli, Alois Ferscha, Sascha Maschek, and Bernhard Wally. Self-aware pervasive service ecosystems. Procedia CS, 7:197–199, 2011.Montagna, Pianini, Viroli (UNIBO) Anticipative Gradient SASO 2012 17 / 18
  • ReferencesGradient-based Self-organisation Patterns of Anticipative Adaptation Sara Montagna, Danilo Pianini and Mirko Viroli sara.montagna@unibo.it Alma Mater Studiorum—Universit` di Bologna a Cesena a Sixth IEEE International Conference on Self-Adaptive and Self-Organizing Systems Lyon, France; 11th September 2012Montagna, Pianini, Viroli (UNIBO) Anticipative Gradient SASO 2012 18 / 18