NLP4: An Architecture for Intent-Driven Data Plane Programmability

Translating high-level policies to lower-level network rules is one of the main goals of control or data plane network programmability. To further abstract requirements and propel automation in networking, several industries have proposed the paradigm of "network intent". However, the tran...

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Vydáno v:IEEE Conference on Network Softwarization (NetSoft) (Online) s. 25 - 30
Hlavní autoři: Angi, Antonino, Sacco, Alessio, Esposito, Flavio, Marchetto, Guido, Clemm, Alexander
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 27.06.2022
Témata:
Automation
Conferences
Industries
load profiling
machine learning
Multilayer perceptrons
Natural language processing
network intent
Propulsion
Prototypes
ISSN:2693-9789
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Shrnutí:Translating high-level policies to lower-level network rules is one of the main goals of control or data plane network programmability. To further abstract requirements and propel automation in networking, several industries have proposed the paradigm of "network intent". However, the translation from intents to low-level policies is considered critical to program data planes and other network elements, especially when dealing with P4-enabled switches. In this paper, we present NLP4, an architecture that helps translate intents, in the form of human language, into data-plane programs, in the form of P4 rules. In particular, NLP4 uses Natural Language Processing (NLP) techniques to translate high-level human-language intents, a MultiLayer Perceptron (MLP) model for processing the NLP output and converting it into mid-level policy. An API then uses this information, which separates the intent from the network to generate commands readable by P4-enabled switches. Our initial prototype on a network emulator validates our architecture for a specific case: load profiling, demonstrating how even users with limited P4 expertise may customize their networks by merely specifying intents.
ISSN:2693-9789
DOI:10.1109/NetSoft54395.2022.9844035