Auto-NFT: Automated Network Function Translator in Virtualized Programmable Data Plane

Hyeim Yang, Seokwon Jang, Sol Han, Sangheon Pack

Research output: Contribution to journalArticlepeer-review


Programmable data plane (PDP) virtualization is a novel technique that enables multiple instances to be supported on a programmable switch. Conventional hypervisor-based virtualization approaches require the hypervisor installation and manual embedding of network functions (NFs), which increases the complexity of PDP virtualization significantly. To address this problem, we propose an automated NF translator (Auto-NFT) that automatically generates and manages the flow rules for a given NF. In this article, we first present background information about the programmable switch and its virtualization. We then describe the design and provide implementation details of Auto-NFT, which was implemented over a commercial programmable switch. The experimental results demonstrate that Auto-NFT outperforms conventional approaches and shows near-optimal performance in terms of the NF embedding success rate and packet processing latency.

Original languageEnglish
Pages (from-to)160-165
Number of pages6
JournalIEEE Network
Issue number2
Publication statusPublished - 2023 Mar 1

Bibliographical note

Funding Information:
This work was supported by National Research Foundation (NRF) grant (No. 2020R1A2C3006786 and 2021R1A4A3022102) funded by the Korean government (MSIT).

Publisher Copyright:
© 1986-2012 IEEE.

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Hardware and Architecture
  • Computer Networks and Communications


Dive into the research topics of 'Auto-NFT: Automated Network Function Translator in Virtualized Programmable Data Plane'. Together they form a unique fingerprint.

Cite this