Recent Advances in Intent-Based Networking: A Survey

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Recent Advances in Intent-Based Networking:
A Survey
Engin Zeydan∗and Yekta Turk
∗Centre Tecnològic de Telecomunicacions de Catalunya (CTTC), Castelldefels, Barcelona, Spain, 08860.
Mobile Network Architect, Istanbul, Turkey, 34396.
engin.zeydan@cttc.cat, yekta.turk@ieee.org
Abstract—This paper investigates the recent-advances in
intent-based technologies while concentrating on aspects related
to network management and orchestration. We provide a com-
prehensive analysis of the standardization activities as well as
platforms related to intent-based networking. At the end of the
paper, we also provide some insights into challenges related
to future development process on the intent-based networking
design. Our survey results indicate that intent-based networking
concept has not evolved further since 2015 in terms of framework,
platform and tool developments. However, recent rapid advances
in Natural Language Understanding (NLU) propelled by IT and
cloud giants (Google, Amazon, Facebook) are expected to increase
its adaption into networking and telecommunication world in the
forthcoming years.
Keywords—intent, network, management, orchestration, survey.
I. INTRODUCTION
Currently, most the operators are involved in complex
network provisioning steps where the configuration and im-
plementation updates are closely related to underlying het-
erogeneous and diverse infrastructure. This brings a necessity
to build abstraction layer on top the network infrastructure
where operators can tune parameters agnostic of the diverse
infrastructure. The main motivation of building an higher-
level management abstraction over complex, heterogeneous
and distributed networks has paved the way for experimenting
solutions of intent-based networking infrastructures over the
recent years. At the same time, as interest in developing
applications using Artificial Intelligence (AI)/Machine Learn-
ing (ML) platforms has risen over last decade due to recent
advancements on deep learning mainly with applications in
Natural Language Processing (NLP) [1] domain, the surge for
intent-based networking is also expected to resurrect after a
dormant period.
Advances in Natural Language Understanding (NLU) sys-
tems together with neural network based algorithms such
as Bidirectional Encoder Representations from Transformers
(BERT), Robustly Optimized BERT pretraining Approach
(RoBERTa), General Language Understanding Evaluation
(GLUE) and Enhanced Representation through kNowledge
IntEgration (ERNIE) have advanced the knowledge to convert
the user queries expressed in a given language (e.g. in English)
into a representation that is adequately structured so that it
can be processed by an automated service [1]. By abstraction
of network services, ever growing complexity of the network
service management as well as corresponding cost levels are
envisioned to be curbed via intents. Recent advancements in AI
can be an important enabler to develop network management
solutions that are driven by intents. Considering the advanced
integration of intent-based technology into existing manage-
ment systems, a three-layered architecture can be designed as
depicted in Fig.1. The main motivation of this architecture
is to introduce intent-based automation to gain new abilities
that would not be in reach with human workforce. In the
business layer, the intents are based on Key Performance Indi-
cators (KPIs) and includes specific Service Level Agreement s
(SLAs), processes, goals, targets or objectives that are triggered
by users from the business layer. The objective of intent layer
is not to execute the full planned sequence of actions blindly,
but re-evaluate and re-plan after every step. The proactively
planned actions must be subjected to approval inside this layer.
In the intent layer of Fig.1, Knowledge handles abstraction
of intents. It also provides the reasoning for intents by building
relationship between objects. Agent in the intent layer provides
an interface to the network objects and performs actions on the
network objects after evaluating the intents. The Agent does
not have intelligence and it sends requests to Knowledge for
reasoning when action is required. The Knowledge informs
the Agent with the composition which is the result obtained
from the analysis of intents. Data observes the network objects
and used for effective storage. Data has the network topology
and inventory information. Data is mainly responsible for
forwarding updates to the Knowledge in a dynamic manner
whenever a new objects is deployed in the network, an object
is de-installed or a topology change occurs. Data provides the
modelling of the network topology and transfer it to the Agent.
The most important state of Data realizing the moving target
and updating the model with more accurate evaluations or if
the business intent is modified. At the bottom of Fig. 1, there is
the network layer that is containing the physical nodes, namely
objects. Its responsibility is to transform the network data into
an formal representation so that intent layer can easily work
with. The abstract model of the hardware is stores in this layer
and it is responsible to execute actions requested by the Agent.
The term “intent” have been in telecommunication industry
over the last 15 years [2]. It is basically adopted as an evolved
version of the term “policy” which dates back to Sloman’s
policy-driven management system in 1994 [3]. The motivation
for this evolution was that policy management was hard and
different entities (e.g. end-users with no technical insights,
app developers that are developing network services without
complicated network interface experience and know-how, op-
erators that are willing to initiate network services in more
abstract and robust manner) wanted much simpler solutions.

Fig. 1: Illustrative intent-based networking architecture.
Intents are defined as a set of specific policy types that are
written in high level operational and business objectives [4].
They are mainly written by humans and should be transformed
into device consumable or lower-level forms. The main idea
behind creating intent-based technologies is to meet the system
requirements without detailing how to achieve these objectives.
Moreover, they are designed to be small in size. Policies are
mainly used to refer to events, conditions and actions which
can include a wide scope. On the other hand different than
policies, intents are mainly used to allow for rules to choose
the behaviour of a system and are considered to be a subset
or type of policy.
Internet Engineering Task Force (IETF) draft in [5] is one
of the few studies that have concentrated on definitions of
policy and intent and their corresponding differences. Policy
is commonly associated to Event-Condition-Action (ECA).
Intents however has been defined as abstract and high-level
policy that are used for network operation in [6]. Hence,
intents can be thought as high-level business or operational
targets that a system should meet. However, the specifications
to achieve these targets are not given, i.e. without any given
characteristics of ECA. For example, consider the case of a
route establishment. A policy can be when a route establish-
ment request between nodes A and B comes in to network
administrator, if the user is C, block the traffic. Then, an intent
can be defined similarly as Routing between nodes A and B for
user C is prohibited. Therefore, the way to enforce the intent
is left to the system.
Some important aspects to consider as characteristics of
intent are [2], [7]: (i) intents are agnostic to underlying hard-
ware and should be portable across technologies. (ii) intents
provide context and are suitable to build a non-conflicting
service deployment. (iii) the intents can be persistent (connect
into database) or transient (transform data into another format).
(iv) the intents are compatible and the requirements are more
explicit and simplified using intents. In network management
and orchestration domain, the first real necessity to design
the infrastructure towards intent-based architecture came along
with the introduction of Software Defined Networking (SDN)
and OpenFlow protocol back in 2014. This was mainly driven
by the requirements of higher-level Application Programming
Interfaces (APIs) where open-source SDN-based projects such
as Open Network Operating System (ONOS) [8] and Open-
DayLight [9] started to work on intent-based APIs.
Our main observations during surveying recent works on
intent-based networking can be summarized as follows. Cur-
rently, the recent frameworks and platforms that provide intent-
based networking are mainly focused inside academia and
not by industry. Therefore, commercial development and de-
ployment of intent driven network management services have
not emerged as yet. On the other hand, major advancements
in AI techniques especially in NLP/NLU areas are expected
to influence the transfer of this knowledge into commercial
products and services in telecommunication and networking
domain in the coming years. Moreover, although various
standardization efforts have emerged during the last decade
together with the introduction of SDN and OpenFlow, the
intent driven network management concept has not progressed
as expected. Therefore, the standardization efforts are currently
at their infancy period. Additionally, the representation or
inference of knowledge from intents are still a major challenge
in both standardization and platform development processes.
The rest of the paper is organized as follows: In Section
II we discuss about the recent standardization activities. In
Section III, we discuss about the developments on intent-based
networking platforms. In Section IV, we discuss about the
outcomes of the survey and give future directions. Finally, in
Section V, we give conclusions.
II. IN TE NT-BASED NETWO RK IN G EFF ORT S IN
STAN DARDIZATIO N
The 3rd Generation Partnership Project (3GPP), European
Telecommunications Standards Institute (ETSI), Open Net-
working Foundation (ONF) and International Telecommuni-
cation Union (ITU) have all developed their own study groups
on intent-based networking. The first standardization effort
for intent-based networking was in ONF back in 2016 [10].
The main idea was to create an intent NorthBound Interface
(NBI) handler that is embedded into or external to network
controller. The Boulder project imitated by ONF to provide
Open Intent NBI. ONF efforts have now merged with ON.Lab
(founders of ONOS controller) which have their own intent-
based interface. The work on TR:28.812-"Study on scenarios
for Intent driven management services for mobile networks"
in Release 16 in the scope of SA5 is 3GPP’s effort on
intent-based network management that started back in 2018
and is still an ongoing effort [11]. This document asserts
the concept of Intent Driven Management (IDM) where an
Intent Driven Management Service (IDMS) is provided to
consumers to manage 5G network and services. Utilization
of intent driven management service is envisioned to originate
from communication service providers/customers and network
operators in the considered scenarios. Some of the considered
scenarios are related to intent driven service deployment,
network provisioning, network optimization, coverage and
capacity management.

ETSI has initiated the Zero-touch network and Service
Management (ZSM) working group [2] for describing means
network automation in 2018. The document provides details
on automation in network management and also concentrates
on policy-driven automation, intent-based automation as well
as intent-based service orchestration in three separate chapters.
This standard document also argues that intent can be applied
to automation in different layers, in APIs, network systems
or service orchestration levels. Another working group within
ETSI under ETSI Experimental Networked Intelligence (ETSI
ENI) considers intent as part of a larger policy classification
(where the other policy classifications are declarative and
imperative policies) [12]. In this standardization report, imper-
ative policies are simply condition-action (CA) or ECA tuples.
Therefore, the order of execution of statements is important.
Declarative policies on the other hand, try to achieve a specific
goal by expressing what need to be done instead of defining its
implementation details. The order of execution of statements
is irrelevant. Intent policies are in fact similar to declarative
policies in the sense of concentrating on what to do instead
of how to do. On the other hand, they are expressed in much
small and simpler policies (3-30 lines) working together and
requires implementation of a translation process, i.e. mapping
(possibly using NLP techniques in practical cases).
ITU-T Study Group 13 explores intent as a declarative
mechanism (written in ML meta-language) where technology-
agnostic ML use case can be deployed by operators inside
their focus group on Machine Learning for Future Network
including 5G (FG-ML5G) [13]. In that sense, intents are used
as high level ML pipeline components. However, building this
meta-language is also foreseen as one of the main challenges in
future implementation of intent-based networking. In summary,
all ongoing standardization activities take intents are high level
specified goals without any specifications on how to execute
the concrete actions. Hence, policies can be derived from
these intents. In some cases such as in ETSI ZSM and ETSI
ENI, intents can also be expressed by human language and
later to be translated into models that can be interpreted by
machines. One of the major challenges that is expressed in
standardization documents is the representation of intent in
terms of language and model specifications which is still an
ongoing and important effort.
III. DEV EL OP ME NT S ON I NT ENT-BASED PLATFORMS
Most of the related solutions on frameworks and platforms
on intent focus on conversion of high-level descriptions into
lower-level configurations, i.e. translatable intents. An intent is
envisioned to translate what into how after its activation and
is continuously monitored to enforce this cycle [4]. Similarly,
another approach to intents is to express them as guidelines to
shape the actions (transform, filter, adapt, etc) of the devices
on a system. This is commonly referred as consultative intents.
The main difference between translatable and consultative
intents arises when feedback mechanism is adapted inside the
system with the consultative intents. Considering the historical
and current status of the system, the enforced and monitored
intents are also adapted. This is commonly applicable for
distributed system management where distributed agents need
to adapt to the dynamism of the system. Consultative and
translatable intents are also referred to as high granularity and
low granularity respectively in some related works [7]. Some
other prior works have also used these two types of intents
together to build an intent-based SDN system [14].
Low granular intents: These intents are mainly used only
for translatable purposes. From a practical direction frame-
works such as ONOS and OpenDayLight SDN controllers have
been working on developing APIs of intent-based networking.
ONOS defines the intent as an immutable model object that
is requested by application to its core and is used to alter
the behaviour of the network in terms of how it operates [8].
ONOS also defines the terms domain intents, which can be
applied to third party controllers that do not directly observe
the devices that are external to their domains. ONOS intent
implementation has also been used or inspired for intent-based
developments in other related works such as using NLP to
specify connectivity requests and convert into ONOS intents in
[15], reconciliation of several intents on top of infrastructure to
enable traffic engineering and optimization of intent framework
in [16], enabling a separate intent framework that can bridge
intent expressions across several domains via including both
KPI constraints and high-level connectivity intentions in [17]
or high level intents translated into ONOS controller that
consider encryption, bandwidth as well as domain constraints
in [18].
OpenDayLight framework also includes sub-projects (e.g.
Network Intent Composition (NIC) [19], NEtwork MOdeling
(NEMO) language in [20] and Group-based Policy (GBP)
abstractions in [21]) that are involved with intent-based net-
working back in 2014 and 2015. For example, NEMO uses a
translation engine to process network languages so that mod-
ules such as OpenFlow can be rendered underneath. Similarly,
intent based NBI of NIC allow descriptive way to obtain
what is desired from the network using any kind of protocols,
including OpenFlow, BGP, Netconf, etc. GBP framework is
designed to provide APIs to capture user intents. However,
those projects are mainly inactive in recent years. Some related
IETF drafts [22], [23] and language extension papers [24] on
NEMO has also been published but the further enhancements
are not further investigated. There exists other controllers that
are been used in the context of intent-based networking such as
a framework called iNDIRA (Intelligent Network Deployment
Intent Renderer Application) in [25], Propane framework in
[26] applied to data center and backbone networks, MD-IDN
for multi-domain environments in [27].
High granular intents: The main idea of high granular
(or consultative) intents is not only translate intents but also
receive feedback that affects these translations and adapt
accordingly. This approach is applied to industrial networks
in [28]. Another form of application of this concept can also
be found in social network graphs. Finding the right intent of
a user query on a social network graph is studied in [29]. This
approach uses network representation learning which uses ML
algorithms to represent networks appropriately which can be
useful to solve implicit context in intent-based networking. An
intent driven networking (IDN) concept is coined in [30] where
hierarchical strictures of mediators are used to keep track of
the intent status and realign accordingly. Software developed
by VeriFlow [31] aims to develop the intent-based networking
from a different perspective by focusing on precisely formal-
izing network behavior by means of measurements and then
verifying it against user intent to avoid any deviations.

TABLE I
CHARACTERISTICS OF LAYERS IN INTENT-BASED NETWORKING ARCHITECTURE AND CORRESPONDING RELATED WORKS.
Layer Characteristics Related Works
— Higher-level declarative policy that operates at the level of a network and services [3], [22], [24]
Business Layer — Provide semantic to consume network resources [6], [31]
— Allowing high-level guidance by a central entity [4], [5], [23]
— Detect and resolve conflicts between multiple intents [7], [8]
Knowledge — Access to knowledge and execute judgement [1], [2]
Intent Layer
— Performs inference from relations between objects [18], [27]
Agent — Capture the business intent and translate into policies [17], [21], [23]
— Utilize ontology-based approach to communicate with users [25]
— Communication interface directly to the network objects [9], [10], [11]
Data — Keep the state of each intent and the relation between network objects [16], [26], [29]
— Provides models for the observed data [13], [20]
— Provides algorithms for data modeling [30]
— Present the abstraction of domain-specific data and control plane technologies [12], [19], [32]
Network Layer — Specify context-aware architecture for enhancing the network intelligence [14], [15], [28]
NLU developments for intents: Recent years have wit-
nessed tremendous efforts and advances in terms of learning
representation of language across a range of diverse domains
and tasks. To evaluate various emerging models, GLUE bench-
mark has been designed [1]. These benchmark tests are aimed
to get closer to human level understanding of the algorithms
and techniques. For this purpose, Convolutional Neural Net-
works (CNN) and Recurrent Neural Networks (RNN) (LSTM,
GRU, etc.)-based architectures and their variations (RNN
seq2seq, CNN seq2seq, seq2seq models with Attention, BERT,
etc.) are trained as a baseline to perform various text analysis
operations on dataset for intent recognition, classification or
slot tagging tasks. For instance, the authors in [32] have
used BERT for training intent classification and slot filling
tasks jointly. On the other hand, in industry Apple’s Siri or
Amazon’s Alexa are some examples of the mostly utilized
related products that are using those advanced NLU models
and techniques.
IV. DISCUSSIONS, CH AL LE NG ES A ND FUTURE
DIRECTIONS
From the recent advances in intent-based networking, we
can extract the following observations: First, differentiating
between event-condition-action policies and the intents is
simplified by concentrating on "what" rather than "how".
Second, intents and their specification languages are also de-
fined separately and corresponding frameworks are developed
within their own use cases. This shows that further efforts on
framework developments are necessary to merge into similar
intent specification languages. Third, as the abstraction level
is increased, e.g. at business layer of Fig. 1, more consultative
intents will be generated. As the intents are progressed down
towards network layer, stronger translatable intents will be
required. Therefore, the co-existences of these two types of
intents can be necessary in some use cases.
There are also multiple challenges during specifications
of intents. The first is to be able to represent intents so that
it can processed and enforced. This requires development of
abstraction of intents as general graphs or trees. Ontologies
and graph databases can be used together to build intent
semantics. In case there exists many intents where some can
be conflicting with each other, elimination of those conflicts
and ensuring increased negotiations between intents between
business, network and intent layers is another related challenge.
To convert intent, translation of human specific language into
machine language is necessary. Recent advances in abilities
of general language understanding using deep neural networks
have come a long way. NLP is a rapidly growing research area
and new algorithms and techniques emerge constantly. Intent
classification (that focuses on predicting the intent of the user
query) and slot filling (used to extract semantic concepts) tasks
in NLU are some relevant examples in this domain [32]. This
has also increased the chance of adoption of these technologies
and ideas into intent-based networking domain.
Although recent developments in AI technologies (espe-
cially in NLP) can help in bringing this gap closer, the
hidden ambiguities and context in human language is still
an impediment for this transformation. Implicit context which
can be clearly understood by humans but not by machines
pose challenges to intent-based networking implementation.
Lack of human labeled data in NLU and NLP tasks is also
a major impediment towards generalization capability of these
techniques. Moreover, most of the pre-built models used in
NLU tasks are built in English language and can also be biased
depending on the collected data used for training the model.
Collecting and annotating a similar corpus as big as English
in another language requires thousands of samples which
need to be both representative and diverse. Depending on
the problems, computational resources can also be significant.
Some models based on NLU tasks require excessive memory
and computationally intensive which can be cumbersome to
deploy in small devices, e.g. on mobile devices. Depending
on the use-case, instead of relying on more complex design
simpler architectures can be utilized during NLU process.
This can provide better trade-off between speed and accuracy.
Finally, privacy of user intents is another issue that need to be
addressed within the framework development processes.
As a summary of related works corresponding to each layer
described in intent-based architecture of Fig. 1, we described
the characteristics of the layers as well as the studies that
are related with each these characteristic in Table 1. As a
future direction, expressing intents concretely in the definitions
of languages and building models that can manipulate and
enforce intents effectively over various domains are some
important study items that need to be addressed in both
standardization activities and framework/platform development
processes. Moreover, continuous/active learning paradigm can

also be introduced as a service to learning agents through a
state action model.
V. CONCLUSIONS
In this survey paper, we provided recent advances in intent-
based networking concentrating on network management and
orchestration. We first provide a comprehensive analysis of
existing frameworks and platforms and later concentrate on
providing the latest activities in standardization domain. Fi-
nally, we discuss about the challenges and future directions
towards building an intent-based networking system for the
telecommunication networks. Our review analysis indicate that
even though the concept of intent-based networking has been
around over the last decades, no major updates have been
observed over the last years. On the other hand, together with
the emergence of AI technologies applied in NLP and NLU
domains, major improvements in intent-based advancements
can be foreseen to be transferred into networking and telecom-
munication world in the upcoming years.
ACKNOWLEDGMENT
This work was funded by Spanish MINECO grant
TEC2017-88373-R (5G-REFINE) and by Generalitat de
Catalunya grant 2017 SGR 1195.
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