Network Working Group A. Clemm
Internet-Draft Futurewei
Intended status: Informational L. Ciavaglia
Expires: March 19, 2021 Nokia
L. Granville
Federal University of Rio Grande do Sul (UFRGS)
J. Tantsura
Apstra, Inc.
September 15, 2020
Intent-Based Networking - Concepts and Definitions
draft-irtf-nmrg-ibn-concepts-definitions-02
Abstract
Intent and Intent-Based Networking (IBN) are taking the industry by
storm. At the same time, those terms are used loosely and often
inconsistently, in many cases overlapping and confused with other
concepts such as "Policy". This document clarifies the concept of
"Intent" and provides an overview of functionality that is associated
with it. The goal is to contribute towards a common and shared
understanding of terms, concepts, and functionality that can be used
as foundation to guide further definition of associated research and
engineering problems and their solutions.
Status of This Memo
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This Internet-Draft will expire on March 19, 2021.
Copyright Notice
Copyright (c) 2020 IETF Trust and the persons identified as the
document authors. All rights reserved.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
2. Key Words . . . . . . . . . . . . . . . . . . . . . . . . . . 4
3. Definitions and Acronyms . . . . . . . . . . . . . . . . . . 4
4. Introduction of Concepts . . . . . . . . . . . . . . . . . . 5
4.1. Intent and Intent-Based Management . . . . . . . . . . . 5
4.2. Related Concepts . . . . . . . . . . . . . . . . . . . . 8
4.2.1. Service Models . . . . . . . . . . . . . . . . . . . 8
4.2.2. Policy and Policy-Based Network Management . . . . . 9
4.2.3. Distinguishing between Intent, Policy, and Service
Models . . . . . . . . . . . . . . . . . . . . . . . 11
5. Principles . . . . . . . . . . . . . . . . . . . . . . . . . 12
6. Intent-Based Networking - Functionality . . . . . . . . . . . 15
6.1. Intent Fulfillment . . . . . . . . . . . . . . . . . . . 16
6.1.1. Intent Ingestion and Interaction with Users . . . . . 16
6.1.2. Intent Translation . . . . . . . . . . . . . . . . . 16
6.1.3. Intent Orchestration . . . . . . . . . . . . . . . . 17
6.2. Intent Assurance . . . . . . . . . . . . . . . . . . . . 17
6.2.1. Monitoring . . . . . . . . . . . . . . . . . . . . . 17
6.2.2. Intent Compliance Assessment . . . . . . . . . . . . 17
6.2.3. Intent Compliance Actions . . . . . . . . . . . . . . 18
6.2.4. Abstraction, Aggregation, Reporting . . . . . . . . . 18
7. Life-cycle . . . . . . . . . . . . . . . . . . . . . . . . . 18
8. Additional Considerations . . . . . . . . . . . . . . . . . . 20
9. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 20
10. Security Considerations . . . . . . . . . . . . . . . . . . . 20
11. References . . . . . . . . . . . . . . . . . . . . . . . . . 22
11.1. Normative References . . . . . . . . . . . . . . . . . . 22
11.2. Informative References . . . . . . . . . . . . . . . . . 22
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 23
1. Introduction
Traditionally in the IETF, interest regarding management and
operations has focused on individual network and device features.
Standardization emphasis has generally been put on management
instrumentation that needed to be provided to a networking device. A
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prime example of this is SNMP-based management and the 200+ MIBs that
have been defined by the IETF over the years. More recent examples
include YANG data model definitions for aspects such as interface
configuration, ACL configuration, or Syslog configuration.
There is a sense and reality that in modern network environments
managing networks by configuring myriads of "nerd knobs" on a device-
by-device basis is no longer sustainable. Significant challenges
arise with keeping device configurations not only consistent across a
network, but consistent with the needs of services and service
features they are supposed to enable. Adaptability to changes at
scale is a fundamental property of a well-designed IBN system, that
requires the ability to consume and process analytics that is
context/intent aware at near real-time speeds. At the same time,
operations need to be streamlined and automated wherever possible to
not only lower operational expenses, but also allow for rapid
reconfiguration of networks at sub-second time scales and to ensure
that networks are delivering their functionality as expected.
Accordingly, the IETF has begun to address end-to-end management
aspects that go beyond the realm of individual devices in isolation.
Examples include the definition of YANG models for network topology
[RFC8345] or the introduction of service models used by service
orchestration systems and controllers [RFC8309]. Much interest has
been fueled by the discussion about how to manage autonomic networks,
as discussed in the ANIMA working group. Autonomic networks are
driven by the desire to lower operational expenses and make the
management of the network as a whole more straightforward, putting it
at odds with the need to manage the network one device and one
feature at a time. However, while autonomic networks are intended to
exhibit "self-management" properties, they still require input from
an operator or outside system to provide operational guidance and
information about the goals, purposes, and service instances that the
network is to serve.
This vision has since caught on with the industry in a big way,
leading to a significant number of solutions that offer "Intent-based
management" that promise network providers to manage networks
holistically at a higher level of abstraction and as a system that
happens to consist of interconnected components, as opposed to a set
of independent devices (that happen to be interconnected). Those
offerings include IBN systems (offering full a life-cycle of intent),
SDN controllers (offering a single point of control and
administration for a network), and network management and Operations
Support Systems (OSS).
However, it has been recognized for a long time that comprehensive
management solutions cannot operate only at the level of individual
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devices and low-level configurations. In this sense, the vision of
"Intent" is not entirely new. In the past, ITU-T's model of a
Telecommunications Management Network, TMN, introduced a set of
management layers that defined a management hierarchy, consisting of
network element, network, service, and business management. High-
level operational objectives would propagate in a top-down fashion
from upper to lower layers. The associated abstraction hierarchy was
crucial to decompose management complexity into separate areas of
concerns. This abstraction hierarchy was accompanied by an
information hierarchy that concerned itself at the lowest level with
device-specific information, but that would, at higher layers,
include, for example, end-to-end service instances. Similarly, the
concept of "Policy-based Network Management (PBNM)" has, for a long
time, touted the ability to allow users to manage networks by
specifying high-level management policies, with policy systems
automatically "rendering" those policies, i.e., breaking them down
into low-level configurations and control logic.
What has been missing, however, is putting these concepts into a more
current context and updating them to account for current technology
trends. This document clarifies the concepts behind intent. It
differentiates it from related concepts. It also provides an
overview of first-order principles of Intent-Based Networking as well
as associated functionality. The goal is to contribute to a common
and shared understanding that can be used as a foundation to
articulate research and engineering problems in the area of Intent-
Based Networking. It should be noted that the articulation of those
problems is beyond this document's scope.
2. Key Words
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
"SHOULD", "SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED", "MAY", and
"OPTIONAL" in this document are to be interpreted as described in BCP
14 [RFC2119] [RFC8174] when, and only when, they appear in all
capitals, as shown here.
3. Definitions and Acronyms
ACL: Access Control List
API: Application Programming Interface
Intent: A set of operational goals that a network should meet and
outcomes that a network is supposed to deliver, defined in a
declarative manner without specifying how to achieve or implement
them.
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IBA: Intent-Based Analytics - Analytics that are defined and
derived from users' intent and used to validate the intended
state.
IBN: Intent-Based Network, a network that can be managed using
intent.
IBS: Intent-Based System, a system that supports management
functions that can be guided using intent.
Policy: A set of rules that governs the choices in behavior of a
system.
PDP: Policy Decision Point
PEP: Policy Enforcement Point
Service Model: A model that represents a service that is provided
by a network to a user.
SSoT: Single Source of Truth - A functional block in an IBN system
that normalizes users' intent and serves as the single source of
data for the lower layers.
4. Introduction of Concepts
The following section provides an overview of the concept of Intent
and Intent-Based Management. It also provides an overview of the
related concepts of service models, and of policies respectively
Policy-Based Network Management, and explains how they relate to
Intent and Intent-Based Management.
4.1. Intent and Intent-Based Management
The term "Intent" was first introduced in the context of Autonomic
Networks, where it is defined as "an abstract, high-level policy used
to operate a network" [RFC7575]. According to this definition, an
Intent is a specific type of policy, provided by a user to provide
guidance to the Autonomic Network that would otherwise operate
without human intervention. However, to avoid using "Intent" simply
as a synonym for "Policy", a distinction needs to be introduced that
differentiates Intent clearly from other types of policies.
For one, while Intent-Based Management aims to lead towards networks
that are dramatically simpler to manage and operate requiring only
minimal outside intervention, the concept of "Intent" is not limited
to autonomic networks, but applies to any network. Networks, even
when considered "autonomic", are not clairvoyant and have no way of
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automatically knowing particular operational goals nor what instances
of networking services to support. In other words, they do not know
what the "Intent" of the network provider is that gives the network
the purpose of its being. This still needs to be communicated by
what informally constitutes "Intent".
More specifically, Intent is a declaration of operational goals that
a network should meet and outcomes that the network is supposed to
deliver, without specifying how to achieve them. Those goals and
outcomes are defined in a manner that is purely declarative - they
specify what to accomplish, not how to achieve it. "Intent" thus
applies several important concepts simultaneously:
o It provides data abstraction: Users and operators do not need to
be concerned with low-level device configuration and nerd knobs.
o It provides functional abstraction from particular management and
control logic: Users and operators do not need to be concerned
even with how to achieve a given Intent. What is specified is a
desired outcome, with the Intent-based system automatically
figuring out a course of action (e.g., a set of rules (thus, a set
of rules is not part of an intent but rather derived from that
intent), an algorithm) for how to achieve the outcome.
The following are some examples of intent:
o "Steer networking traffic originating from endpoints in one
geography away from a second geography, unless the destination
lies in that second geography."
o "Avoid routing networking traffic originating from a given set of
endpoints (or associated with a given customer) through a
particular vendor's equipment, even if this occurs at the expense
of reduced service levels."
o "Maximize network utilization even if it means trading off service
levels (such as latency, loss), unless service levels have
deteriorated 20% or more from their historic mean."
o "VPN service must have path protection at all times for all
paths."
o "Generate in-situ OAM data and network telemetry across for later
offline analysis whenever significant fluctuations in latency
across a path are observed."
In an autonomic network, intent should be rendered by the network
itself, i.e., translated into device-specific rules and courses of
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action. Ideally, it should not even be orchestrated or broken down
by a higher-level, centralized system, but by the network devices
themselves using a combination of distributed algorithms and local
device abstraction. In this idealized vision, because intent holds
for the network as a whole, intent should ideally be automatically
disseminated across all devices in the network, which can themselves
decide whether they need to act on it.
However, such decentralization will not be practical in all cases.
Certain functions will need to be at least conceptually centralized.
For example, users may require a single conceptual point of
interaction with the network. Likewise, the vast majority of network
devices may be intent-agnostic and focus only (for example) on the
actual forwarding of packets. This implies that certain intent
functionality needs to be provided by functions that are specialized
for that purpose (which depending on scenario may be hosted on
dedicated systems, or cohosted with other networking functions). For
example, functionality to translate intent into courses of actions
and algorithms to achieve desired outcomes may need to be provided by
such specialized functions. Of course, to avoid single points of
failure, the implementation and hosting of those functions may still
itself be distributed, even if conceptually centralized.
Accordingly, an intent-based network is a network that can be managed
using intent. This means, it is able to recognize and ingest intent
of an operator, or user, and configure and adapt itself autonomously
according to the user intent, achieving an intended outcome (i.e., a
desired state or behavior) without requiring the user to specify the
detailed technical steps for how to achieve the outcome. Instead,
the intent-based network will be able to figure out on its own how to
achieve the outcome.
Other definitions of intent exist, such as [TR523]. Intent there is
simply defined as a declarative interface that is typically provided
by a controller. It implies the presence of a centralized function
that renders the intent into lower-level policies or instructions and
orchestrates them across the network. While this is certainly one
way of implementation, the definition presented here is narrower in
the sense that it emphasizes the importance of managing the network
by specifying desired outcomes without the specific steps to be taken
in order to achieve the outcome. A controller API that simply
provides a network-level of abstraction would not necessarily qualify
as intent. Likewise, ingestion and recognition of intent may not
necessarily occur via a traditional API, but may involve other types
of human-machine interactions.
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4.2. Related Concepts
4.2.1. Service Models
A service model is a model that represents a service that is provided
by a network to a user. Per [RFC8309], a service model describes a
service and its parameters in a portable/implementation-agnostic way
that can be used independently of the equipment and operating
environment on which the service is realized. Two subcategories are
distinguished: a "Customer Service Model" describes an instance of a
service as provided to a customer, possibly associated with a service
order. A "Service Delivery Model" describes how a service is
instantiated over existing networking infrastructure.
An example of a service could be a Layer 3 VPN service [RFC8299], a
Network Slice, or residential Internet access. Service models
represent service instances as entities in their own right. Services
have their own parameters, actions, and life-cycles. Typically,
service instances can be bound to end-users, who might be billed for
the service.
Instantiating a service typically involves multiple aspects:
o A user (or northbound system) needs to define and/or request a
service to be instantiated.
o Resources need to be allocated, such as IP addresses, AS numbers,
VLAN or VxLAN pools, interfaces, bandwidth, or memory.
o How to map services to the resources needs to be defined.
Multiple mappings are often possible, which to select may depend
on context (such as which type of access is available to connect
the end user with the service).
o Bindings need to be maintained between upper and lower-level
objects.
o Once instantiated, the service needs to be validated and assured
to ensure that the network indeed delivers the service as
requested.
They involve a system, such as a controller, that provides
provisioning logic. This includes breaking down high-level
abstractions into lower-level device abstractions, identifying and
allocating system resources, and orchestrating individual
provisioning steps. Orchestration operations are generally conducted
using a "push" model in which the controller/manager initiates the
operations as required, then pushes down the specific configurations
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to the device. In addition to instantiating and creating new
instances of a service, updating, modifying, and decommissioning
services need to be also supported. The device itself typically
remains agnostic to the service or the fact that its resources or
configurations are part of a service/concept at a higher layer.
Instantiated service models map to instantiated lower-layer network
and device models. Examples include instances of paths, or instances
of specific port configurations. The service model typically also
models dependencies and layering of services over lower-layer
networking resources that are used to provide services. This
facilitates management by allowing to follow dependencies for
troubleshooting activities, to perform impact analysis in which
events in the network are assessed regarding their impact on services
and customers. Services are typically orchestrated and provisioned
top-to-bottom, which also facilitates keeping track of the assignment
of network resources. Service models might also be associated with
other data that does not concern the network but provides business
context. This includes things such as customer data (such as billing
information), service orders and service catalogs, tariffs, service
contracts, and Service Level Agreements (SLAs), including contractual
agreements regarding remediation actions.
[I-D.ietf-teas-te-service-mapping-yang] is an example of a data model
that provides a mapping for customer service models (e.g., the L3VPN
Service Model) to Traffic Engineering (TE) models (e.g., the TE
Tunnel or the Abstraction and Control of Traffic Engineered Networks
Virtual Network model)
Like intent, service models provide higher layers of abstraction.
Service models are often also complemented with mappings that capture
dependencies between service and device or network configurations.
Unlike intent, service models do not allow to define a desired
"outcome" that would be automatically maintained by the intent
system. Instead, the management of service models requires the
development of sophisticated algorithms and control logic by network
providers or system integrators.
4.2.2. Policy and Policy-Based Network Management
Policy-Based Network Management (PBNM) is a management paradigm that
separates the rules that govern the behavior of a system from the
functionality of the system. It promises to reduce maintenance costs
of information and communication systems while improving flexibility
and runtime adaptability. It is present today at the heart of a
multitude of management architectures and paradigms, including SLA-
driven, Business-driven, autonomous, adaptive, and self-* management
[Boutaba07]. The interested reader is asked to refer to the rich set
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of existing literature, which includes this and many other
references. In the following, we will only provide a much-abridged
and distilled overview.
At the heart of policy-based management is the concept of a policy.
Multiple definitions of policy exist: "Policies are rules governing
the choices in the behavior of a system" [Sloman94]. "Policy is a
set of rules that are used to manage and control the changing and/or
maintaining of the state of one or more managed objects"
[Strassner03]. Common to most definitions is the definition of a
policy as a "rule". Typically, the definition of a rule consists of
an event (whose occurrence triggers a rule), a set of conditions
(which get assessed and which must be true before any actions are
actually "fired"), and finally a set of one or more actions that are
carried out when the condition holds.
Policy-based management can be considered an imperative management
paradigm: Policies precisely specified what needs to be done when and
in which circumstance. By using policies, management can, in effect,
be defined as a set of simple control loops. This makes policy-based
management a suitable technology to implement autonomic behavior that
can exhibit self-* management properties, including self-
configuration, self-healing, self-optimization, and self-protection.
In effect, policies define management as a set of simple control
loops.
Policies typically involve a certain degree of abstraction in order
to cope with the heterogeneity of networking devices. Rather than
having a device-specific policy that defines events, conditions, and
actions in terms of device-specific commands, parameters, and data
models, a policy is defined at a higher-level of abstraction
involving a canonical model of systems and devices to which the
policy is to be applied. A policy agent on a controller or the
device subsequently "renders" the policy, i.e., translates the
canonical model into a device-specific representation. This concept
allows applying the same policy across a wide range of devices
without needing to define multiple variants. In other words - policy
definition is de-coupled from policy instantiation and policy
enforcement. This enables operational scale and allows network
operators and authors of policies to think in higher terms of
abstraction than device specifics and be able to reuse the same,
high-level definition across different networking domains, WAN, DC,
or public cloud.
PBNM is typically "push-based": Policies are pushed onto devices
where they are rendered and enforced. The push operations are
conducted by a manager or controller, which is responsible for
deploying policies across the network and monitor their proper
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operation. That being said, other policy architectures are possible.
For example, policy-based management can also include a pull-
component in which the decision regarding which action to take is
delegated to a so-called Policy Decision Point (PDP). This PDP can
reside outside the managed device itself and has typically global
visibility and context with which to make policy decisions. Whenever
a network device observes an event that is associated with a policy,
but lacks the full definition of the policy or the ability to reach a
conclusion regarding the expected action, it reaches out to the PDP
for a decision (reached, for example, by deciding on an action based
on various conditions). Subsequently, the device carries out the
decision as returned by the PDP - the device "enforces" the policy
and hence acts as a PEP (Policy Enforcement Point). Either way, PBNM
architectures typically involve a central component from which
policies are deployed across the network, and/or policy decisions
served.
Like Intent, policies provide a higher layer of abstraction. Policy
systems are also able to capture dynamic aspects of the system under
management through the specification of rules that allow defining
various triggers for specific courses of actions. Unlike intent, the
definition of those rules (and courses of actions) still needs to be
articulated by users. Since the intent is unknown, conflict
resolution within or between policies requires interactions with a
user or some kind of logic that resides outside of PBM. In that
sense, policy constitutes a lower level of abstraction than intent,
and it is conceivable for Intent-Based Systems to generate policies
that are subsequently deployed by a PBM, allowing PBM to support
Intent-Based Networking.
4.2.3. Distinguishing between Intent, Policy, and Service Models
What Intent, Policy, and Service Models all have in common is the
fact that they involve a higher-layer of abstraction of a network
that does not involve device-specifics, that generally transcends
individual devices, and that makes the network easier to manage for
applications and human users compared to having to manage the network
one device at a time. Beyond that, differences emerge. Service
models have less in common with policy and intent than policy and
intent do with each other.
Summarized differences:
o A service model is a data model that is used to describe instances
of services that are provided to customers. A service model has
dependencies on lower-level models (device and network models)
when describing how the service is mapped onto underlying network
and IT infrastructure. Instantiating a service model requires
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orchestration by a system; the logic for how to
orchestrate/manage/provide the service model, and how to map it
onto underlying resources, is not included as part of the model
itself.
o Policy is a set of rules, typically modeled around a variation of
events/conditions/actions, used to express simple control loops
that can be rendered by devices, without requiring intervention by
the outside system. Policy lets users define what to do under
what circumstances, but it does not specify the desired outcome.
o Intent is a high-level, declarative goal that operates at the
level of a network and services it provides, not individual
devices. It is used to define outcomes and high-level operational
goals, without specifying how those outcomes and should be
achieved or how goals should specifically be satisfied, and
without the need to enumerate specific events, conditions, and
actions. Which algorithm or rules to apply can be automatically
"learned/derived from intent" by the intent system. In the
context of autonomic networking, intent is ideally rendered by the
network itself; also, the dissemination of intent across the
network and any required coordination between nodes is resolved by
the network without the need for external systems.
One analogy to capture the difference between policy and intent
systems is that of Expert Systems and Learning Systems in the field
of Artificial Intelligence. Expert Systems operate on knowledge
bases with rules that are supplied by users, analogous to policy
systems whose polies are supplied by users. They are able to make
automatic inferences based on those rules, but are not able to
"learn" new rules on their own. Learning Systems (popularized by
deep learning and neural networks), on the other hand, are able to
learn without depending on user programming or articulation of rules.
However, they do require a learning or training phase, and
explanations of actions that the system actually takes provide a
different set of challenges. Analogous to intent-based systems,
users focus on what they would like the learning system to
accomplish, but not how to do it.
5. Principles
The following main operating principles allow characterizing the
intent-based/-driven/-defined nature of a system.
1. Single Source of Truth (SSoT) and Single Version/View of Truth
(SVoT). The SSoT is an essential component of an intent-based
system as it enables several important operations. The set of
validated intent expressions is the system's SSoT. SSoT and the
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records of the operational states enable comparing the intended
state and actual state of the system and determining drift
between them. SSoT and the drift information provide the basis
for corrective actions. If the intent-based is equipped with
prediction capabilities or means, it can further develop
strategies to anticipate, plan, and pro-actively act on the
diverging trends with the aim to minimize their impact. Beyond
providing a means for consistent system operation, SSoT also
allows for better traceability to validate if/how the initial
intent and associated business goals have been properly met, to
evaluate the impacts of changes in the intent parameters and
impacts and effects of the events occurring in the system.
Single Version (or View) of Truth derives from the SSoT and can
be used to perform other operations such as query, poll, or
filter the measured and correlated information to create so-
called "views". These views can serve the operators and/or the
users of the intent-based system. To create intents as single
sources of truth, the intent-based system must follow well-
specified and well-documented processes and models. In other
contexts [Lenrow15], SSoT is also referred to as the invariance
of the intent.
2. One-touch but not one-shot. In an ideal intent-based system, the
user expresses its intents in one form or another, and then the
system takes over all subsequent operations (one-touch). A zero-
touch approach could also be imagined in the case where the
intent-based system has the capabilities or means to recognize
intentions in any form of data. However, the zero- or one-touch
approach should not be mistaken the fact that reaching the state
of a well-formed and valid intent expression is not a one-shot
process. On the contrary, the interfacing between the user and
the intent-based system could be designed as an interactive and
iterative process. Depending on the level of abstraction, the
intent expressions will initially contain more or less implicit
parts, and unprecise or unknown parameters and constraints. The
role of the intent-based system is to parse, understand, and
refine the intent expression to reach a well-formed and valid
intent expression that can be further used by the system for the
fulfillment and assurance operations. An intent refinement
process could use a combination of iterative steps involving the
user to validate the proposed refined intent and to ask the user
for clarifications in case some parameters or variables could not
be deduced or learned by the means of the system itself. In
addition, the Intent-Based System will need to moderate between
conflicting intent, helping users to properly choose between
intent alternatives that may have different ramifications.
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3. Autonomy and Supervision. A desirable goal for an intent-based
system is to offer a high degree of flexibility and freedom on
both the user side and system side, e.g., by giving the user the
ability to express intents using its own terms, by supporting
different forms of expression of intents and being capable of
refining the intent expressions to well-formed and exploitable
expressions. The dual principle of autonomy and supervision
allows to operate a system that will have the necessary levels of
autonomy to conduct its tasks and operations without requiring
intervention of the user and taking its own decisions (within its
areas of concern and span of control) as how to perform and meet
the user expectations in terms of performance and quality, while
at the same time providing the proper level of supervision to
satisfy the user requirements for reporting and escalation of
relevant information.
4. Learning. An intent-based system is a learning system. By
contrast to the imperative type of system, such as Event-
Condition-Action policy rules, where the user defines beforehand
the expected behavior of the system to various events and
conditions, in an intent-based system, the user only declares
what the system should achieve and not how to achieve these
goals. There is thus a transfer of reasoning/rationality from
the human (domain knowledge) to the system. This transfer of
cognitive capability also implies the availability in the intent-
based system of capabilities or means for learning, reasoning,
and knowledge representation and management. The learning
abilities of an intent-based systems can apply to different tasks
such as optimization of the intent rendering or intent refinement
processes. The fact that an intent-based system is a
continuously evolving system creates the condition for continuous
learning and optimization. Other cognitive capabilities such as
planning can also be leveraged in an intent-based system to
anticipate or forecast future system state and response to
changes in intents or network conditions and thus elaboration of
plans to accommodate the changes while preserving system
stability and efficiency in a trade-off with cost and robustness
of operations. Cope with unawareness of users (smart
recommendations).
5. Capability exposure. Capability exposure consists in the need
for expressive network capabilities, requirements, and
constraints to be able to compose/decompose intents and map the
user's expectations to the system capabilities.
6. Abstract and outcome-driven. Users do not need to be concerned
with how intent is achieved and are empowered to think in terms
of outcomes. In addition, they do can refer to concepts at a
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higher level of abstractions, independent e.g. of vendor-specific
renderings.
The described principles are perhaps the most prominent, but they are
not an exhaustive list. There are additional aspects to consider,
such as:
o Intent targets are not individual devices but typically
aggregations (such as groups of devices adhering to a common
criteria, such as devices of a particular role) or abstractions
(such as service types, service instances, topologies)
o Abstraction and inherent virtualization: agnostic to
implementation details
o Human-tailored network interaction: IBN SHOULD speak the language
of the user as opposed to requiring the user to speak the language
of the device/network
o Explainability as an important IBN function, detection and IBN-
aided resolution of conflicting intent, reconciliation of what the
user wants and what the network can actually do
o Inherent support, verification, and assurance of trust
All of these principles and considerations have implications on the
design of intent-based systems and their supporting architecture and
need to be considered when deriving functional and operational
requirements.
6. Intent-Based Networking - Functionality
Intent-Based Networking involves a wide variety of functions which
can be roughly divided into two categories:
o Intent Fulfillment provides functions and interfaces that allow
users to communicate intent to the network, and that perform the
necessary actions to ensure that intent is achieved. This
includes algorithms to determine proper courses of action and
functions that learn to optimize outcomes over time. In addition,
it also includes more traditional functions such as any required
orchestration of coordinated configuration operations across the
network and rendering of higher-level abstractions into lower-
level parameters and control knobs.
o Intent Assurance provides functions and interfaces that allow
users to validate and monitor that the network is indeed adhering
to and complying with intent. This is necessary to assess the
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effectiveness of actions taken as part of fulfillment, providing
important feedback that allows those functions to be trained or
tuned over time to optimize outcomes. In addition, Intent
Assurance is necessary to address "intent drift". Intent drift
occurs when a system originally meets the intent, but over time
gradually allows its behavior to change or be affected until it no
longer does, or does so in a less effective manner.
The following sections provide a more comprehensive overview of those
functions.
6.1. Intent Fulfillment
Intent fulfillment is concerned with the functions that take intent
from its origination by a user (generally, an administrator of the
responsible organization) to its realization in the network.
6.1.1. Intent Ingestion and Interaction with Users
The first set of functions is concerned with "ingesting" intent, i.e.
obtaining intent through interactions with users. They provide
functions that recognize intent from interaction with the user as
well as functions that allow users to refine their intent and
articulate it in such ways so that it becomes actionable by an
Intent-Based System. Typically, those functions go beyond a
traditional API, although they may include APIs provided for
interactions with other machines. They may support unconventional
human-machine interactions, in which a human will not simply give
simple commands, but which may involve a human-machine dialog to
provide clarifications, to explain ramifications and trade-offs, and
to facilitate refinements. The goal is ultimately to make intent-
based systems as easy and natural to use as possible, allowing the
user to interact with the Intent-Based System in ways that does not
involve a steep learning curve forcing the user to learn the
"language" of the system
6.1.2. Intent Translation
A second set of functions needs to translate user intent into courses
of actions and requests to take against the network, which will be
meaningful to network configuration and provisioning systems. These
functions lie at the core of Intent-Based Systems, bridging the gap
between interaction with users on one hand and the traditional
management and operations side that will need to orchestrate
provisioning and configuration across the network.
Beyond merely breaking down a higher layer of abstraction (intent)
into a lower layer of abstraction (policies, device configuration),
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Intent Translation functions can be complemented with functions and
algorithms that perform optimizations and that are able to learn and
improve over time in order to result in the best outcomes,
specifically in cases where multiple ways of achieving those outcomes
are conceivable. For example, satisfying an intent may involve
computation of paths and other parameters that need will need to be
configured across the network. Heuristics and algorithms to do so
may evolve over time to optimize outcomes which may depend a myriad
of dynamic network conditions and context.
6.1.3. Intent Orchestration
A third set of functions deals with the actual configuration and
provisioning steps that need to be orchestrated across the network
and that were determined by the previous intent translation step.
6.2. Intent Assurance
Assurance is concerned with the functions that are necessary to
ensure that the network indeed complies with the desired intent once
it has been fulfilled.
6.2.1. Monitoring
A first set of assurance functions monitors and observes the network
and its exhibited behavior. This includes all the usual assurance
functions such as monitoring the network for events and performance
outliers, performing measurements to assess service levels that are
being delivered, generating and collecting telemetry data.
Monitoring and observation are required as basis for the next set of
functions that assess whether the observed behavior is in fact in
compliance with the behavior that is expected based on the intent.
6.2.2. Intent Compliance Assessment
At the core of Intent Assurance are functions that compare the actual
network behavior that is being monitored and observed with the
intended behavior that is expected per the intent. These functions
continuously assess and validate whether the observation indicates
compliance with intent. This includes assessing the effectiveness of
intent fulfillment actions, including verifying that the actions had
the desired effect and assessing the magnitude of the effect as
applicable. It can also include functions that perform analysis and
aggregation of raw observation data. The results of the assessment
can be fed back to facilitate learning functions that optimize
outcomes.
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Intent compliance assessment also includes assessing whether intent
drift occurs over time. Intent drift can be caused by control plane
or lower-level management operations that inadvertently cause
behavior changes which conflict with intent which was orchestrated
earlier. Intent-Based Systems and Networks need to be able to detect
when such drift occurs or is about to occur.
6.2.3. Intent Compliance Actions
When intent drift occurs or network behavior is inconsistent with
desired intent, functions that are able to trigger corrective actions
are needed. This includes actions needed to resolve intent drift and
bring the network back into compliance. Alternatively and where
necessary, reporting functions need to be triggered that alert
operators and provide them with the necessary information and tools
to react appropriately, e.g. by helping them articulate modifications
to the original intent to moderate between conflicting concerns.
6.2.4. Abstraction, Aggregation, Reporting
The outcome of Intent Assurance needs to be reported back to the user
in ways that allows the user to relate the outcomes to their intent.
This requires a set of functions that are able to analyze, aggregate,
and abstract the results of the observations accordingly. In many
cases, lower-level concepts such as detailed performance statistics
and observations related to low-level settings need to be "up-
leveled" to concepts the user can relate to and take action on.
The required aggregation and analysis functionality needs to be
complemented with functions that report intent compliance status and
provide adequate summarization and visualization to the user.
7. Life-cycle
Intent is subject to a life-cycle: it comes into being, may undergo
changes over the course of time, and may at some point be retracted.
This life-cycle is closely tied to various interconnection functions
that are associated with the intent concept.
Figure 1 depicts an intent life-cycle and its main functions. The
functions were introduced in Section 6 and are divided into two
functional (horizontal) planes, reflecting the distinction between
fulfillment and assurance. In addition, they are divided into three
(vertical) spaces.
The spaces indicate the different perspectives and interactions with
different roles that are involved in addressing the functions:
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o The user space involves the functions that interface the network
and intent-based system with the human user. It involves the
functions that allow users to articulate and the intent-based
system to recognize that intent. It also involves the functions
that report back the status of the network relative to the intent
and that allow users to assess whether their intent has the
desired effect.
o The translation or Intent-Based System (IBS) space involves the
functions that bridge the gap between intent users and network
operations. This includes the functions used to translate an
intent into a course of action, the algorithms used to plan and
optimize those courses of actions also in consideration of
feedback, the functions to analyze and abstract observations to
validate compliance with the intent and take corrective actions as
necessary.
o The Network Operations space, finally, involves the traditional
orchestration, configuration, monitoring, and measurement
functions, which are used to effectuate the rendered intent and
observe its effects on the network.
User Space : Translation / IBS : Network Ops
: Space : Space
: :
+----------+ : +----------+ +-----------+ : +-----------+
Fulfill |recognize/|---> |translate/|-->| learn/ |-->| configure/|
|generate | | | | plan/ | | provision |
|intent |<--- | refine | | render | : | |
+----^-----+ : +----------+ +-----^-----+ : +-----------+
| : | : |
.............|................................|................|.....
| : +----+---+ : v
| : |validate| : +----------+
| : +----^---+ <----| monitor/ |
Assure +---+---+ : +---------+ +-----+---+ : | observe/ |
|report | <---- |abstract |<---| analyze | <----| |
+-------+ : +---------+ |aggregate| : +----------+
: +---------+ :
Figure 1: Intent Life-cycle
When carefully inspecting the diagram, it becomes apparent that the
intent life-cycle, in fact, involves two cycles, or loops:
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o The "inner" intent control loop between IBS and Network Operations
space is completely autonomic and does not involve any human in
the loop. It involves automatic analysis and validation of intent
based on observations from the network operations space. Those
observations are fed into the function that plans the rendering of
networking intent in order to make adjustments as needed in the
configuration of the network.
o The "outer" intent control loop involves the user space and
includes the user taking action and adjusting their intent based
on feedback from the IBS.
8. Additional Considerations
Given the popularity of the term "intent", its use could be broadened
to encompass also known or related concepts, resulting in "intent-
washing" that paints those concepts in a new light by simply applying
new intent terminology to them. However, in some cases, this
actually makes sense not just a marketing ploy but as a way to better
relate previously existing and new concepts.
In that sense and regards to intent, it make sense to distinguish
various subcategories of intent as follows:
o Operational Intent: defines intent related to operational goals of
an operator; corresponds to the original "intent" term and the
concepts defined in this document.
o Rule Intent: a synonym for policy rules regarding what to do when
certain events occur.
o Service intent: a synonym for customer service model [RFC8309].
o Flow Intent: A synonym for a Service Level Objective for a given
flow.
A comprehensive set of classifications of different concepts and
categories of intent will be described in a separate document.
9. IANA Considerations
Not applicable
10. Security Considerations
This document describes concepts and definitions of Intent-based
Networking. As such, the below security considerations remain high
level, i.e. in the form of principles, guidelines or requirements.
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More detailed security considerations will be described in the
documents that specify the architecture and functionality.
Security in Intent-based Networking can apply to different facets:
o Securing the intent-based system itself.
o Mitigating the effects of erroneous, harmful or compromised
intents.
o Expressing security policies or security-related parameters with
intents.
Securing the intent-based system aims at making the intent-based
system operationally secure by implementing security mechanisms and
applying security best practices. In the context of Intent-based
Networking, such mechanisms and practices may consist in intent
verification and validation; operations on intents by authenticated
and authorized users only; protection against or detection of
tampered intents. Such mechanisms may also include the introduction
of multiple levels of intent. For example, intent related to
securing the network should occur at a "deeper" level that overrides
other levels of intent if necessary, and that is not subject to
modification through regular operations but through ones that are
specifically secured. Use of additional mechanisms such as
explanation components that describe the security ramifications and
trade-off should be considered as well.
Mitigating the effects of erroneous or compromised intents aims at
making the intent-based system operationally safe by providing
checkpoint and safeguard mechanisms and operating principles. In the
context of Intent-based Networking, such mechanisms and principles
may consist in the ability to automatically detect unintended,
detrimental or abnormal behavior; the ability to automatically (and
gracefully) rollback or fallback to a previous "safe" state; the
ability to prevent or contain error amplification (due to the
combination of higher degree of automation and the intrinsic higher
degree of freedom, ambiguity, and implicit conveyed by intents);
dynamic levels of supervision and reporting to make the user aware of
the right information, at the right time with the right level of
context. Erroneous or harmful intents may inadvertently propagate
and compromise security. In addition, compromised intents, for
example intent forged by an inside attacker, may sabotage or harm the
network resources and make them vulnerable to further, larger
attacks, e.g. by defeating certain security mechanisms.
Expressing security policies or security-related parameters with
intents consists in using the intent formalism (a high-level,
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declarative abstraction), or part(s) of an intent statement to define
security-related aspects such as data governance, level(s) of
confidentiality in data exchange, level(s) of availability of system
resources, of protection in forwarding paths, of isolation in
processing functions, level(s) of encryption, authorized entities for
given operations, etc.
The development and introduction of Intent-Based Networking in
operational environments will certainly create new security concerns.
Such security concerns have to be anticipated at the design and
specification time. However, Intent-Based Networking may also be
used as an enabler for better security. For instance, security and
privacy rules could be expressed in more human-friendly and generic
way and be less technology-specific and less complex, leading to
fewer low-level configuration mistakes. The detection of threats or
attacks could also be made more simple and comprehensive thanks to
conflict detection at higher-level or at coarser granularity
More thorough security analyses should be conducted as our
understanding of Intent-Based Networking technology matures.
11. References
11.1. Normative References
[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", BCP 14, RFC 2119,
DOI 10.17487/RFC2119, March 1997,
<https://www.rfc-editor.org/info/rfc2119>.
[RFC8174] Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC
2119 Key Words", BCP 14, RFC 8174, DOI 10.17487/RFC8174,
May 2017, <https://www.rfc-editor.org/info/rfc8174>.
11.2. Informative References
[Boutaba07]
Boutaba, R. and I. Aib, "Policy-Based Management: A
Historical perspective. Journal of Network and Systems
Management (JNSM), Springer, Vol. 15 (4).", December 2007.
[I-D.ietf-teas-te-service-mapping-yang]
Lee, Y., Dhody, D., Fioccola, G., WU, Q., Ceccarelli, D.,
and J. Tantsura, "Traffic Engineering (TE) and Service
Mapping Yang Model", draft-ietf-teas-te-service-mapping-
yang-04 (work in progress), July 2020.
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[Lenrow15]
Lenrow, D., "Intent As The Common Interface to Network
Resources, Intent Based Network Summit 2015 ONF Boulder:
IntentNBI", February 2015.
[RFC7575] Behringer, M., Pritikin, M., Bjarnason, S., Clemm, A.,
Carpenter, B., Jiang, S., and L. Ciavaglia, "Autonomic
Networking: Definitions and Design Goals", RFC 7575,
DOI 10.17487/RFC7575, June 2015,
<https://www.rfc-editor.org/info/rfc7575>.
[RFC8299] Wu, Q., Ed., Litkowski, S., Tomotaki, L., and K. Ogaki,
"YANG Data Model for L3VPN Service Delivery", RFC 8299,
DOI 10.17487/RFC8299, January 2018,
<https://www.rfc-editor.org/info/rfc8299>.
[RFC8309] Wu, Q., Liu, W., and A. Farrel, "Service Models
Explained", RFC 8309, DOI 10.17487/RFC8309, January 2018,
<https://www.rfc-editor.org/info/rfc8309>.
[RFC8345] Clemm, A., Medved, J., Varga, R., Bahadur, N.,
Ananthakrishnan, H., and X. Liu, "A YANG Data Model for
Network Topologies", RFC 8345, DOI 10.17487/RFC8345, March
2018, <https://www.rfc-editor.org/info/rfc8345>.
[Sloman94]
Sloman, M., "Policy Driven Management for Distributed
Systems. Journal of Network and Systems Management (JNSM),
Springer, Vol. 2 (4).", December 1994.
[Strassner03]
Strassner, J., "Policy-Based Network Management.
Elsevier.", 2003.
[TR523] Foundation, O. N., "Intent NBI - Definition and
Principles. ONF TR-523.", October 2016.
Authors' Addresses
Alexander Clemm
Futurewei
2330 Central Expressway
Santa Clara, CA 95050
USA
Email: ludwig@clemm.org
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Laurent Ciavaglia
Nokia
Route de Villejust
Nozay 91460
FR
Email: laurent.ciavaglia@nokia.com
Lisandro Zambenedetti Granville
Federal University of Rio Grande do Sul (UFRGS)
Av. Bento Goncalves
Porto Alegre 9500
BR
Email: granville@inf.ufrgs.br
Jeff Tantsura
Apstra, Inc.
Email: jefftant.ietf@gmail.com
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