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)1-8
Number of pages8
JournalIEEE Network
Publication statusAccepted/In press - 2022


  • Noise measurement
  • Pipelines
  • Process control
  • Switches
  • Throughput
  • Virtual machine monitors
  • Virtualization

ASJC Scopus subject areas

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


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