NFlow and MVT Abstractions for NFV Scaling

Ziyan Wu; Yang Zhang; Wendi Feng; Zhi Li Zhang

doi:10.1109/INFOCOM48880.2022.9796764

NFlow and MVT Abstractions for NFV Scaling

Ziyan Wu, Yang Zhang, Wendi Feng, Zhi Li Zhang

Computer Science and Engineering

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

5 Scopus citations

Abstract

The ability to dynamically scale in/out network functions (NFs) on multiple cores/servers to meet traffic demands is a key benefit of network function virtualization (NFV). The stateful NF operations make NFV scaling a challenging task: if care is not taken, NFV scaling can lead to incorrect operations and poor performance. We advocate two general abstractions, NFlow and Match-Value Table (MVT), for NFV packet processing pipelines. We present formal definitions of the abstractions and discuss how they can facilitate NFV scaling by minimizing or eliminating shared states. Using NFs implemented with the proposed abstractions, we conduct extensive experiments and demonstrate their efficacy in terms of correctness and performance of NFV scaling.

Original language	English (US)
Title of host publication	INFOCOM 2022 - IEEE Conference on Computer Communications
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	180-189
Number of pages	10
ISBN (Electronic)	9781665458221
DOIs	https://doi.org/10.1109/INFOCOM48880.2022.9796764
State	Published - 2022
Event	41st IEEE Conference on Computer Communications, INFOCOM 2022 - Virtual, Online, United Kingdom Duration: May 2 2022 → May 5 2022

Publication series

Name	IEEE INFOCOM 2022 - IEEE Conference on Computer Communications

Conference

Conference	41st IEEE Conference on Computer Communications, INFOCOM 2022
Country/Territory	United Kingdom
City	Virtual, Online
Period	5/2/22 → 5/5/22

Bibliographical note

Funding Information:
ACKNOWLEDGEMENT The research was supported in part by NSF under Grants CNS-1814322, CNS-1831140, CNS-1836772, CNS-1901103, CNS-2106771 and CCF-2123987.C

Publisher Copyright:
© 2022 IEEE.

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10.1109/INFOCOM48880.2022.9796764

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NFlow and MVT Abstractions for NFV Scaling. / Wu, Ziyan; Zhang, Yang; Feng, Wendi et al.
INFOCOM 2022 - IEEE Conference on Computer Communications. Institute of Electrical and Electronics Engineers Inc., 2022. p. 180-189 (IEEE INFOCOM 2022 - IEEE Conference on Computer Communications).

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

Wu, Z, Zhang, Y, Feng, W & Zhang, ZL 2022, NFlow and MVT Abstractions for NFV Scaling. in INFOCOM 2022 - IEEE Conference on Computer Communications. IEEE INFOCOM 2022 - IEEE Conference on Computer Communications, Institute of Electrical and Electronics Engineers Inc., pp. 180-189, 41st IEEE Conference on Computer Communications, INFOCOM 2022, Virtual, Online, United Kingdom, 5/2/22. https://doi.org/10.1109/INFOCOM48880.2022.9796764

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abstract = "The ability to dynamically scale in/out network functions (NFs) on multiple cores/servers to meet traffic demands is a key benefit of network function virtualization (NFV). The stateful NF operations make NFV scaling a challenging task: if care is not taken, NFV scaling can lead to incorrect operations and poor performance. We advocate two general abstractions, NFlow and Match-Value Table (MVT), for NFV packet processing pipelines. We present formal definitions of the abstractions and discuss how they can facilitate NFV scaling by minimizing or eliminating shared states. Using NFs implemented with the proposed abstractions, we conduct extensive experiments and demonstrate their efficacy in terms of correctness and performance of NFV scaling.",

author = "Ziyan Wu and Yang Zhang and Wendi Feng and Zhang, {Zhi Li}",

note = "Funding Information: ACKNOWLEDGEMENT The research was supported in part by NSF under Grants CNS-1814322, CNS-1831140, CNS-1836772, CNS-1901103, CNS-2106771 and CCF-2123987.C Publisher Copyright: {\textcopyright} 2022 IEEE.; 41st IEEE Conference on Computer Communications, INFOCOM 2022 ; Conference date: 02-05-2022 Through 05-05-2022",

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N2 - The ability to dynamically scale in/out network functions (NFs) on multiple cores/servers to meet traffic demands is a key benefit of network function virtualization (NFV). The stateful NF operations make NFV scaling a challenging task: if care is not taken, NFV scaling can lead to incorrect operations and poor performance. We advocate two general abstractions, NFlow and Match-Value Table (MVT), for NFV packet processing pipelines. We present formal definitions of the abstractions and discuss how they can facilitate NFV scaling by minimizing or eliminating shared states. Using NFs implemented with the proposed abstractions, we conduct extensive experiments and demonstrate their efficacy in terms of correctness and performance of NFV scaling.

AB - The ability to dynamically scale in/out network functions (NFs) on multiple cores/servers to meet traffic demands is a key benefit of network function virtualization (NFV). The stateful NF operations make NFV scaling a challenging task: if care is not taken, NFV scaling can lead to incorrect operations and poor performance. We advocate two general abstractions, NFlow and Match-Value Table (MVT), for NFV packet processing pipelines. We present formal definitions of the abstractions and discuss how they can facilitate NFV scaling by minimizing or eliminating shared states. Using NFs implemented with the proposed abstractions, we conduct extensive experiments and demonstrate their efficacy in terms of correctness and performance of NFV scaling.

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NFlow and MVT Abstractions for NFV Scaling

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