PCL: Packet classification with limited knowledge

Vitalii Demianiuk; Chen Hajaj; Kirill Kogan

doi:10.1109/INFOCOM42981.2021.9488751

PCL: Packet classification with limited knowledge

Vitalii Demianiuk, Chen Hajaj, Kirill Kogan

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

2 Scopus citations

Abstract

We introduce a novel representation of packet classifiers allowing to operate on partially available input data varying dynamically. For a given packet classifier, availability of fields or complexity of field computations, and free target specific resources, the proposed infrastructure computes a classifier representation satisfying performance and robustness requirements. We show the feasibility to reconstruct a classification result in this noisy environment, allowing for the improvement of performance and the achievement of additional robustness levels of network infrastructure. Our results are supported by extensive evaluations in various settings where only a partial input is available.

Original language	English
Title of host publication	INFOCOM 2021 - IEEE Conference on Computer Communications
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9780738112817
DOIs	https://doi.org/10.1109/INFOCOM42981.2021.9488751
State	Published - 10 May 2021
Event	40th IEEE Conference on Computer Communications, INFOCOM 2021 - Vancouver, Canada Duration: 10 May 2021 → 13 May 2021

Publication series

Name	Proceedings - IEEE INFOCOM
Volume	2021-May
ISSN (Print)	0743-166X

Conference

Conference	40th IEEE Conference on Computer Communications, INFOCOM 2021
Country/Territory	Canada
City	Vancouver
Period	10/05/21 → 13/05/21

Access to Document

10.1109/INFOCOM42981.2021.9488751

Cite this

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title = "PCL: Packet classification with limited knowledge",

abstract = "We introduce a novel representation of packet classifiers allowing to operate on partially available input data varying dynamically. For a given packet classifier, availability of fields or complexity of field computations, and free target specific resources, the proposed infrastructure computes a classifier representation satisfying performance and robustness requirements. We show the feasibility to reconstruct a classification result in this noisy environment, allowing for the improvement of performance and the achievement of additional robustness levels of network infrastructure. Our results are supported by extensive evaluations in various settings where only a partial input is available.",

author = "Vitalii Demianiuk and Chen Hajaj and Kirill Kogan",

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Demianiuk, V, Hajaj, C & Kogan, K 2021, PCL: Packet classification with limited knowledge. in INFOCOM 2021 - IEEE Conference on Computer Communications., 9488751, Proceedings - IEEE INFOCOM, vol. 2021-May, Institute of Electrical and Electronics Engineers Inc., 40th IEEE Conference on Computer Communications, INFOCOM 2021, Vancouver, Canada, 10/05/21. https://doi.org/10.1109/INFOCOM42981.2021.9488751

PCL: Packet classification with limited knowledge. / Demianiuk, Vitalii; Hajaj, Chen; Kogan, Kirill.
INFOCOM 2021 - IEEE Conference on Computer Communications. Institute of Electrical and Electronics Engineers Inc., 2021. 9488751 (Proceedings - IEEE INFOCOM; Vol. 2021-May).

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

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N2 - We introduce a novel representation of packet classifiers allowing to operate on partially available input data varying dynamically. For a given packet classifier, availability of fields or complexity of field computations, and free target specific resources, the proposed infrastructure computes a classifier representation satisfying performance and robustness requirements. We show the feasibility to reconstruct a classification result in this noisy environment, allowing for the improvement of performance and the achievement of additional robustness levels of network infrastructure. Our results are supported by extensive evaluations in various settings where only a partial input is available.

AB - We introduce a novel representation of packet classifiers allowing to operate on partially available input data varying dynamically. For a given packet classifier, availability of fields or complexity of field computations, and free target specific resources, the proposed infrastructure computes a classifier representation satisfying performance and robustness requirements. We show the feasibility to reconstruct a classification result in this noisy environment, allowing for the improvement of performance and the achievement of additional robustness levels of network infrastructure. Our results are supported by extensive evaluations in various settings where only a partial input is available.

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PCL: Packet classification with limited knowledge

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