AI Routers & Network Mind: A Hybrid Machine Learning Paradigm for Packet Routing

Haipeng Yao, Tianle Mai, Chunxiao Jiang, Linling Kuang, Song Guo

Research output: Journal article publicationJournal articleAcademic researchpeer-review

18 Citations (Scopus)

Abstract

With the increasing complexity of network topologies and architectures, adding intelligence to the network control plane through Artificial Intelligence and Machine Learning (AI&ML) is becoming a trend in network development. For large-scale geo-distributed systems, determining how to appropriately introduce intelligence in networking is the key to high-efficiency operation. In this treatise, we explore two deployment paradigms (centralized vs. distributed) for AI-based networking. To achieve the best results, we propose a hybrid ML paradigm that combines a distributed intelligence, based on units called "AI routers," with a centralized intelligence, called the "network mind", to support different network services. In the proposed paradigm, we deploy centralized AI control for connection-oriented tunneling-based routing protocols (such as multiprotocol label switching and segment routing) to guarantee a high QoS, whereas for hop-by-hop IP routing, we shift the intelligent control responsibility to each AI router to ease the overhead imposed by centralized control and use the network mind to improve the global convergence.

Original languageEnglish
Article number8870277
Pages (from-to)21-30
Number of pages10
JournalIEEE Computational Intelligence Magazine
Volume14
Issue number4
DOIs
Publication statusPublished - Nov 2019

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Artificial Intelligence

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