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Gossip algorithms can be made more efficient by optimizing network topology rather than the algorithm itself. This study identifies key network features that impact gossip algorithm performance for resource-efficient communication.

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Area of Science:

  • Computer Science
  • Network Engineering
  • Distributed Systems

Background:

  • Gossip algorithms facilitate robust, scalable network communication through randomized interactions.
  • Inefficiency arises from redundant information propagation, increasing resource consumption.
  • Prior research focused on algorithm optimization and performance bounds.

Purpose of the Study:

  • To characterize the impact of network topology on gossip algorithm performance.
  • To enable performance engineering by modifying network structure, not just the algorithm.
  • To provide guidelines for designing resource-efficient networks.

Main Methods:

  • Numerical experiments to identify topological limiting factors and predictive graph metrics.
  • Analysis of algorithm efficiency across different graph families.
  • Regression analyses to validate the influence of structural features on performance.

Main Results:

  • Identified specific topological features limiting gossip algorithm performance.
  • Determined the most predictive graph metrics for performance estimation.
  • Found that network structure significantly explains performance variations.
  • Demonstrated the effectiveness of local network metrics for performance prediction.

Conclusions:

  • Network topology is a critical factor in gossip algorithm efficiency.
  • Optimizing network structure offers a viable approach to enhance performance.
  • A novel distributed method for performance prediction based on local topology is proposed.