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Related Experiment Video

Updated: Nov 23, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Information theory inspired optimization algorithm for efficient service orchestration in distributed systems.

Matheus Sant'Ana Lima1

  • 1Department of Computer Science, Federal University of São Carlos, São Paulo, Brasil.

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This study introduces a novel algorithm for optimizing network routing in distributed systems, inspired by Algorithmic Information Theory. The new method effectively finds near-optimal routes, improving efficiency for cloud computing and data management.

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Area of Science:

  • Computer Science
  • Mathematics
  • Statistics

Background:

  • Distributed systems are the standard for cloud computing, processing vast amounts of information.
  • Current architectures shift from client-server to multitier designs for better data management.
  • Efficient service deployment across geographically dispersed data centers presents a significant routing challenge.

Purpose of the Study:

  • To propose a new optimization algorithm for selecting optimum routes in distributed systems.
  • To address the network routing challenge, modeled as a variation of the Travelling Salesman Problem (TSP).
  • To leverage Algorithmic Information Theory for reliable quantitative route optimization.

Main Methods:

  • The study proposes a novel algorithm based on Algorithmic Information Theory.
  • It utilizes the Kelly criterion for a Shannon-Bernoulli process to generate a quantitative algorithm.
  • The algorithm's performance is verified against benchmark heuristic solutions and analyzed statistically.

Main Results:

  • The proposed algorithm demonstrates improved solution quality, yielding routes with reduced length and time.
  • It offers flexibility across problems of varying complexity, outperforming nature-inspired models.
  • Statistical analysis confirms the significance of the results and the derivation of entropy functions.

Conclusions:

  • The developed algorithm provides a near-optimal solution for network routing in distributed systems.
  • It offers a more efficient approach to service deployment across large clusters of nodes.
  • The findings unify Computer Science, Mathematics, and Statistics, advancing the understanding of entropy in decision-making.