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No free lunch for avoiding clustering vulnerabilities in distributed systems.

Pheerawich Chitnelawong1, Andrei A Klishin2,3, Norman Mackay1

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Complex systems often fail due to element clustering. This study reveals repulsion-driven clustering in heterogeneous networks, offering new strategies to enhance design resilience and manage vulnerabilities.

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Area of Science:

  • Statistical physics applied to complex systems
  • Network science and emergent phenomena
  • Engineering design and resilience

Background:

  • Emergent design failures are common in complex systems, often linked to element clustering.
  • Reducing clustering is challenging when driven by collective interactions.
  • Understanding clustering mechanisms is key to improving system resilience.

Purpose of the Study:

  • To identify mechanisms of spatial cluster emergence in complex systems.
  • To investigate clustering in heterogeneous networks using statistical physics.
  • To develop a framework for managing clustering vulnerabilities in design.

Main Methods:

  • Modeling complex systems with heterogeneous networks.
  • Applying techniques from statistical physics.
  • Analyzing emergent clustering phenomena, including repulsion-driven clustering.

Main Results:

  • Identified both attraction-driven and emergent repulsion-driven clustering in heterogeneous networks.
  • Demonstrated quantitative connections between naval engineering models and nanoscale self-assembly.
  • Showed that clustering phenomena are general across many distributed systems.

Conclusions:

  • Heterogeneous networks exhibit complex clustering behaviors beyond simple attraction.
  • Understanding repulsion-driven clustering is crucial for designing resilient complex systems.
  • A framework is presented to quantify trade-offs and manage design vulnerabilities related to clustering.