Network Tomography and Reinforcement Learning for Efficient Routing

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Network tomography is a powerful tool to infer the internal state of a network using end-to-end metrics observed by a few nodes at the edge of the network. However, previous research in network tomography has not focused on the objectives and challenges of specific network management applications, resulting in unsatisfactory performance. This paper proposes Subito (Shortest Path Routing with Multi-armed Bandits and Network Tomography) to address the needs and challenges of shortest path routing, a cornerstone of many network management tasks in wired and wireless networks. Subito combines network tomography with reinforcement learning to find an efficient routing strategy. Experiments on synthetic networks show that Subito provides performance improvements up to three times compared to two state-of-the-art approaches.

Original languageEnglish
Title of host publicationProceedings - 2023 IEEE 20th International Conference on Mobile Ad Hoc and Smart Systems, MASS 2023
Pages384-389
Number of pages6
ISBN (Electronic)9798350324334
DOIs
StatePublished - 2023
Event20th IEEE International Conference on Mobile Ad Hoc and Smart Systems, MASS 2023 - Toronto, Canada
Duration: Sep 25 2023Sep 27 2023

Publication series

NameProceedings - 2023 IEEE 20th International Conference on Mobile Ad Hoc and Smart Systems, MASS 2023

Conference

Conference20th IEEE International Conference on Mobile Ad Hoc and Smart Systems, MASS 2023
Country/TerritoryCanada
CityToronto
Period9/25/239/27/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

  • Efficient Routing
  • Multi-armed Bandits
  • Network Tomography
  • Reinforcement Learning

ASJC Scopus subject areas

  • Control and Optimization
  • Modeling and Simulation
  • Instrumentation
  • Artificial Intelligence
  • Computer Networks and Communications
  • Information Systems and Management

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