@INPROCEEDINGS{Angi2206:NLP4,
AUTHOR="Antonino Angi and Alessio Sacco and Flavio Esposito and Guido Marchetto and
Alexander Clemm",
TITLE="{NLP4:} An Architecture for {Intent-Driven} Data Plane Programmability",
BOOKTITLE="2022 2nd International Workshop on Intent-based Networking (WIN 2022)",
ADDRESS="Milan, Italy",
DAYS="30",
MONTH=jun,
YEAR=2022,
KEYWORDS="network intent; load profiling; machine learning",
ABSTRACT={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.}
}