ReactNet: self-adjusting architecture for networked systems.

Habib Mostafaei, Seyed Milad Miri, Stefan Schmid

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

    1 Citation (Scopus)

    Abstract

    Providers today run numerous applications on their networks with diverse quality of service requirements. An appealing vision to deal with the resulting complexity of network operation, is to give more control to the network, allowing it to become more autonomous and to dynamically "self-adjust", to meet its requirements. This paper presents an architecture, ReactNet, to realize this vision, by leveraging two enabling technologies. First, we use programmable dataplanes and P4 to get accurate information about the traffic patterns the network currently serves. Second, we leverage Machine Learning (ML) techniques to process this information and react to the network changes dynamically.

    Original languageEnglish
    Title of host publicationCoNEXT 2021 - Proceedings of the 17th International Conference on emerging Networking EXperiments and Technologies
    Pages473-474
    Number of pages2
    ISBN (Electronic)9781450390989
    DOIs
    Publication statusPublished - 2 Dec 2021

    Bibliographical note

    DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.

    Keywords

    • Machine learning
    • Programmable dataplanes
    • Self-adjusting networks

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