ReactNet: self-adjusting architecture for networked systems.

Habib Mostafaei; Seyed Milad Miri; Stefan Schmid

doi:10.1145/3485983.3493348

ReactNet: self-adjusting architecture for networked systems.

Habib Mostafaei, Seyed Milad Miri, Stefan Schmid

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

3 Citations (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 language	English
Title of host publication	CoNEXT 2021 - Proceedings of the 17th International Conference on emerging Networking EXperiments and Technologies
Pages	473-474
Number of pages	2
ISBN (Electronic)	9781450390989
DOIs	https://doi.org/10.1145/3485983.3493348
Publication status	Published - 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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10.1145/3485983.3493348

Cite this

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title = "ReactNet: self-adjusting architecture for networked systems.",

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.",

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AU - Miri, Seyed Milad

AU - Schmid, Stefan

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PY - 2021/12/2

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N2 - 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.

AB - 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.

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KW - Programmable dataplanes

KW - Self-adjusting networks

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M3 - Conference contribution

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EP - 474

BT - CoNEXT 2021 - Proceedings of the 17th International Conference on emerging Networking EXperiments and Technologies

ER -

ReactNet: self-adjusting architecture for networked systems.

Abstract

Bibliographical note

Keywords

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