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Epidemiology-based Task Assignment Algorithm for Distributed Systems.

Parth Brahmbhatt1, Sergio G Camorlinga1

  • 1Applied Computer Science Department, University of Winnipeg, 515 Portage Ave, Winnipeg, MB R3G 3J6, Canada.

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Summary
This summary is machine-generated.

This study models task assignment as an epidemic in distributed systems. Understanding disease spread patterns can create effective distributed algorithms for complex task delegation.

Keywords:
Agent Based ModellingComputational EpidemiologyDistributed AlgorithmsEpidemiologyModellingTask Assignment

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Area of Science:

  • Computational epidemiology
  • Distributed systems engineering
  • Algorithm design

Background:

  • Task assignment in distributed systems is complex and can yield suboptimal results.
  • Epidemics spread through population activities and environment, exhibiting emergent behavior.
  • Epidemiology studies spatiotemporal patterns of illness and influencing factors.

Purpose of the Study:

  • To design task assignment algorithms inspired by disease spread patterns.
  • To investigate task assignment as an epidemic within a distributed system.
  • To leverage insights from disease outbreak mechanisms for algorithmic development.

Main Methods:

  • Utilized agent-based modeling to simulate aerosol-borne disease outbreaks.
  • Analyzed emergent patterns during simulated disease spread among individuals.
  • Developed computational models to understand disease propagation dynamics.

Main Results:

  • Identified mechanisms of disease emergence and spread in populations.
  • Demonstrated the potential for disease spread patterns to inform algorithm design.
  • Gained insights into the complex interactions driving emergent behaviors.

Conclusions:

  • Understanding emergent disease behavior provides a foundation for novel distributed algorithms.
  • These algorithms can address challenges in task assignment within distributed systems.
  • The research offers a new perspective on optimizing distributed task delegation.