Intent-Based Networking - Concepts and Definitions
draft-irtf-nmrg-ibn-concepts-definitions-06
Network Working Group A. Clemm
Internet-Draft Futurewei
Intended status: Informational L. Ciavaglia
Expires: June 18, 2022 Rakuten Mobile
L. Granville
Federal University of Rio Grande do Sul (UFRGS)
J. Tantsura
Microsoft
December 15, 2021
Intent-Based Networking - Concepts and Definitions
draft-irtf-nmrg-ibn-concepts-definitions-06
Abstract
Intent and Intent-Based Networking (IBN) are taking the industry by
storm. At the same time, IBN-related terms are often used loosely
and 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 the 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 the foundation to guide further definition of associated
research and engineering problems and their solutions.
This document is a product of the IRTF Network Management Research
Group (NMRG). It reflects the consensus of the RG, receiving reviews
and explicit support from many members. It is published for
informational purposes.
Status of This Memo
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This Internet-Draft will expire on June 18, 2022.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 3
2. Definitions and Acronyms . . . . . . . . . . . . . . . . . . 5
3. Introduction of Concepts . . . . . . . . . . . . . . . . . . 6
3.1. Intent and Intent-Based Management . . . . . . . . . . . 6
3.2. Related Concepts . . . . . . . . . . . . . . . . . . . . 10
3.2.1. Service Models . . . . . . . . . . . . . . . . . . . 10
3.2.2. Policy and Policy-Based Network Management . . . . . 12
3.2.3. Distinguishing between Intent, Policy, and Service
Models . . . . . . . . . . . . . . . . . . . . . . . 14
4. Principles . . . . . . . . . . . . . . . . . . . . . . . . . 15
5. Intent-Based Networking - Functionality . . . . . . . . . . . 18
5.1. Intent Fulfillment . . . . . . . . . . . . . . . . . . . 18
5.1.1. Intent Ingestion and Interaction with Users . . . . . 18
5.1.2. Intent Translation . . . . . . . . . . . . . . . . . 19
5.1.3. Intent Orchestration . . . . . . . . . . . . . . . . 20
5.2. Intent Assurance . . . . . . . . . . . . . . . . . . . . 20
5.2.1. Monitoring . . . . . . . . . . . . . . . . . . . . . 20
5.2.2. Intent Compliance Assessment . . . . . . . . . . . . 20
5.2.3. Intent Compliance Actions . . . . . . . . . . . . . . 21
5.2.4. Abstraction, Aggregation, Reporting . . . . . . . . . 21
6. Lifecycle . . . . . . . . . . . . . . . . . . . . . . . . . . 21
7. Additional Considerations . . . . . . . . . . . . . . . . . . 23
8. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 24
9. Security Considerations . . . . . . . . . . . . . . . . . . . 24
10. Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . 25
11. Informative References . . . . . . . . . . . . . . . . . . . 26
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 28
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1. Introduction
This document is a product of the IRTF Network Management Research
Group (NMRG). It reflects the consensus of the RG, receiving reviews
and explicit support from many members. It is published for
informational purposes.
In the past, interest regarding management and operations in the IETF
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
prime example of this is SNMP-based management [RFC3411] and the 200+
MIBs that have been defined by the IETF over the years. More recent
examples include YANG data model definitions [RFC7950] for aspects
such as interface configuration, ACL configuration, and Syslog
configuration.
There is a clear sense and reality that managing networks by
configuring myriads of "nerd knobs" on a device-by-device basis is no
longer an option in modern network environments. Significant
challenges arise with keeping device configurations not only
consistent across a network but also consistent with the needs of
services and service features they are supposed to enable.
Additional challenges arise with regards to being able to rapidly
adapt the network as needed and to be able to do so at scale. 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.
Among other things, this requires the ability to consume operational
data, perform analytics, and dynamically take actions in a way that
is aware of context as well as intended outcomes at near real-time
speeds.
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 of the
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
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system to provide operational guidance and information about the
goals, purposes, and service instances that the network is to serve.
This input and operational guidance are commonly referred to as
"intent," and networks that allow network operators to provide their
input using intent as "Intent-Based Networks" (IBN) and the systems
that help implement intent as "Intent-Based Systems" (IBS). However,
intent is about more than just enabling a form of operator
interaction with the network that involves higher-layer abstractions.
It is also about the ability to let operators focus on what they want
their desired outcomes to be while leaving details about how those
outcomes would, in fact, be achieved to the IBN (respectively IBS).
This, in turn, enables much greater operational efficiency at greater
scale, in shorter time scales, with less dependency on human
activities (and possibility for mistakes), and an ideal candidate,
e.g., for artificial intelligence techniques that can bring about the
next level of network automation [Clemm20].
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 and IBS (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).
It has been recognized for a long time that comprehensive management
solutions cannot operate only at the level of individual 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 [M3010]. 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
concern. 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. (As a note, in the
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context of this document, "users" generally refers to operators and
administrators who are responsible for the management and operation
of communication services and networking infrastructures, not to "end
users" of communication services.)
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 intent from related concepts. It also provides an
overview of first-order principles of IBN as well as the 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 IBN.
It should be noted that the articulation of IBN-related research
problems is beyond the scope of this document. However, it should be
recognized that IBN has become an important topic in the research
community. Per IEEE Xplore [IEEEXPLORE], as of December 2021, in the
past decade since 2012, there have been 1138 papers with the index
term "intent", of which 411 specifically mention networking. The
time period since 2020 alone accounts for 316 papers on intent and
153 for intent networking, indicating accelerating interest. In
addition, workshops dedicated to this theme are beginning to appear,
such as the IEEE International Workshop on Intent-Based Networking
[WIN21], as well as various special journal issues [IEEE-TITS21]
[MDPI22]. A survey of current intent-driven networking research has
been published in [Pang20], listing among the most pressing current
research challenges aspects such as intent translation and
understanding, intent interfaces, and security.
2. 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.
IBA: Intent-Based Analytics - Analytics that are defined and
derived from users' intent and used to validate the intended
state.
Intent-Based Management - The concept of performing management
based on the concept of intent.
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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.
PBNM: Policy-Based Network Management
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.
SVoT: Single Version of Truth
User: In the context of this document, an operator and/or
administrator responsible for the management and operation of
communication services and networking infrastructure (as opposed
to an end user of a communication service)
3. 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 policies (and Policy-Based
Network Management), and explains how they relate to intent and
Intent-Based Management.
3.1. Intent and Intent-Based Management
In this document, intent is defined as a set of operational goals
(that a network is supposed to meet) and outcomes (that a network is
supposed to deliver), defined in a declarative manner without
specifying how to achieve or implement them.
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
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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 that differentiates intent
clearly from other types of policies needs to be introduced.
Intent-Based Management aims to lead towards networks that are
fundamentally simpler to manage and operate, requiring only minimal
outside intervention. Networks, even when they are autonomic, are
not clairvoyant and have no way of automatically knowing particular
operational goals nor which 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 to the network by what informally
constitutes intent. That being said, the concept of intent is not
limited just to autonomic networks, such as networks that feature an
Autonomic Control Plane [RFC8994], but applies to any network.
More specifically, intent is a declaration of a set of operational
goals that a network is supposed to meet and outcomes that the
network is supposed to deliver, without specifying how to achieve or
how to implement 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 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 do not need to be concerned even with how to
achieve a given intent. What is specified is the desired outcome,
with the IBS automatically figuring out a course of action (e.g.,
using an algorithm or applying a set of rules derived from the
intent) for how to achieve the outcome.
The following are some examples of intent, expressed in natural
language for the sake of clarity (actual interfaces used to convey
intent may differ):
o "Steer networking traffic originating from endpoints in one
geography away from a second geography, unless the destination
lies in that second geography." (States what to achieve, not
how.)
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." (States what to achieve, not how,
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providing additional guidance for how to trade off between
different goals when necessary)
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 historical mean." (A desired
outcome, with a set of constraints for additional guidance, which
does not specify how to achieve this.)
o "VPN service must have path protection at all times for all
paths." (A desired outcome of which it may not be clear how it
can be precisely accommodated.)
o "Generate in-situ OAM data and network telemetry for later offline
analysis whenever significant fluctuations in latency across a
path are observed." (Goes beyond traditional event-condition-
action by not being specific about what constitutes "significant"
and what specific data to collect)
o "Route traffic in a Space Information Network in a way that
minimizes dependency on stratospheric balloons unless the intended
destination is an aircraft." (Does not specify how to precisely
achieve this; extrapolates on scenarios mentioned in [Pang20])
o "For a smart city service, ensure traffic signal control traffic
uses dedicated and redundant slices that avoid fate sharing." (A
desired outcome with a set of constraints and additional guidance
without specifying how to precisely achieve this; extrapolates on
scenarios from [Gharbaoui21]).
In contrast, the following are examples of what would not constitute
intent (again, expressed in natural language for the sake of
clarity):
o "Configure a given interface with an IP address." This would be
considered device configuration and fiddling with configuration
knobs, not intent.
o "When interface utilization exceeds a specific threshold, emit an
alert." This would be a rule that can help support network
automation, but a simple rule is not an intent.
o "Configure a VPN with a tunnel from A to B over path P." This
would be considered as a configuration of a service.
o "Deny traffic to prefix P1 unless it is traffic from prefix P2."
This would be an example of an access policy or a firewall rule,
not intent.
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In networks, in particular in networks that are deemed autonomic,
intent should ideally be rendered by the network itself, i.e.,
translated into device-specific rules and courses of action.
Ideally, intent would not need to 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 abstractions. 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. Many devices may also be constrained
in their processing resources and hence their ability to act on
intent. Depending on the intent, not every devices needs to be aware
of certain intent, for example when it does not affect them and when
they play no role in achieving the desired outcome. In addition,
certain functions may benefit from global knowledge of a network and
its state, which may not be known or too expensive to maintain or
access from individual network devices. This implies that certain
intent functionality needs to be provided by functions that are
specialized for that purpose (which, depending on the scenario, may
be hosted on dedicated systems or co-hosted with other networking
functions). For example, the translation of specific types of intent
into corresponding courses of action and algorithms to achieve the
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 be
distributed, even if conceptually centralized.
Accordingly, an IBN is a network that can be managed using intent.
This means that it is able to recognize and ingest intent of an
operator or user and configure and adapt itself 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 IBN
will be able to figure out on its own how to achieve the outcome.
Similarly, an IBS is a system that allows users to manage a network
using intent. Such a system will serve as a point of interaction
with users and implement the functionality that is necessary to
achieve the intended outcomes, interacting for that purpose with the
network as required.
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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 that is presented here is more
encompassing and ambitious, as 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 is
more limited and 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.
3.2. Related Concepts
3.2.1. 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 [I-D.ietf-teas-ietf-network-slice-definition], 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 of communication services, who might be
billed for the services provided.
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, such as IP addresses, AS numbers, VLAN or VxLAN pools,
interfaces, bandwidth, or memory need to be allocated.
o How to map services to the resources needs to be defined.
Multiple mappings are often possible, which to select may depend
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on context (such as which type of access is available to connect
the end user of a communication service with the service).
o Bindings between upper and lower-level objects need to be
maintained.
o Once instantiated, the service operational state needs to be
validated and assured to ensure that the network indeed delivers
the service as requested.
The realization of service models involves a system, such as a
controller, that provides provisioning logic. This includes breaking
down high-level service 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 to the device and validates whether
the new changes have been accepted and the new operational/derived
states are achieved and in sync with the intent/desired state. 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 (composition), while troubleshooted bottom-up
(decomposition). 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
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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 an IBS. Instead,
the management of service models requires the development of
sophisticated algorithms and control logic by network providers or
system integrators.
3.2.2. 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
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.
This is notwithstanding the fact that policy-based management may
make use of the concept of abstractions (such as, "Bob gets gold
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service") that hide from the user the specifics of how that
abstraction is rendered in a particular deployment.
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 monitoring their proper
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 action. Unlike intent, the
definition of those rules (and courses of action) still needs to be
articulated by users. Since the intent is unknown, conflict
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resolution within or between policies requires interactions with a
user or some kind of logic that resides outside of PBNM. 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 PBNM system, allowing PBNM to
support Intent-Based Networking.
3.2.3. 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.
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 the underlying
network and IT infrastructure. Instantiating a service model
requires 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. Policies let users define what to do under
what circumstances, but they do 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 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 IBS. 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.
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One analogy to capture the difference between policy and Intent-Based
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 policies 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 requiring large
data sets; 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.
4. 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
records of the operational states enable comparing the intended/
desired state and actual/operational states of the system and
determining drift between them. SSoT and the drift information
provide the basis for corrective actions. If the IBS is equipped
with the means to predict states, it can further develop
strategies to anticipate, plan, and pro-actively act on any
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 querying, polling,
or filtering measured and correlated information in order to
create so-called "views." These views can serve the users of the
intent-based system. In order to create intents as single
sources of truth, the IBS must follow well-specified and well-
documented processes and models. In other contexts, SSoT is also
referred to as the invariance of the intent [Lenrow15].
2. One-touch but not one-shot. In an ideal intent-based system, the
user expresses intent in one form or another, and then the system
takes over all subsequent operations (one-touch). A zero-touch
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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 distract from 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 may 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 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.
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 the user's 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 operating a system that will have the
necessary levels of autonomy to conduct its tasks and operations
without requiring the 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. In
contrast to an 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 is supposed to 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
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intent-based system of capabilities or means for learning,
reasoning, and knowledge representation and management. The
learning abilities of an IBS 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.
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 can refer to concepts at a 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.
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All of these principles and considerations have implications on the
design of intent-based systems and their supporting architecture.
Accordingly, they need to be considered when deriving functional and
operational requirements.
5. Intent-Based Networking - Functionality
Intent-Based Networking involves a wide variety of functions that 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
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 is not
meant to be transactional, i.e. "set and forget", but expected to
be remain in effect over time (unless explicitly stated
otherwise). 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.
5.1. 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.
5.1.1. 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
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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 those
provided by a traditional API, although APIs may still be provided
(and needed for interactions beyond human users, i.e., with other
machines). Many cases would also involve a set of intuitive and
easy-to-navigate workflows that guide users through the intent
ingestion phase, making sure that all inputs that are necessary for
intent modeling and consecutive translation have been gathered. 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 IBSs as easy and natural to use and
interact with as possible, in particular allowing human users to
interact with the IBS in ways that do not involve a steep learning
curve that forces the user to learn the "language" of the system.
Ideally, it will be the Intent-Based Systems that is increasingly be
able to learn how to understand the user as opposed to the other way
round. Of course, further research will be required to make this a
reality.
5.1.2. Intent Translation
A second set of functions needs to translate user intent into courses
of action and requests to take against the network, which will be
meaningful to network configuration and provisioning systems. These
functions lie at the core of IBS, bridging the gap between
interaction with users on the 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),
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 will need to be
configured across the network. Heuristics and algorithms to do so
may evolve over time to optimize outcomes that may depend on a myriad
of dynamic network conditions and context.
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5.1.3. 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.
5.2. Intent Assurance
Intent 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.
5.2.1. 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 the 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.
5.2.2. 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 and is held by
SSoT. 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.
Intent compliance assessment also includes assessing whether intent
drift occurs over time. Intent drift can be caused by a control
plane or lower-level management operations that inadvertently cause
behavior changes which conflict with intent that was orchestrated
earlier. Intent-Based Systems and Networks need to be able to detect
when such drift occurs or is about to occur as well as assess the
severity of the drift.
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5.2.3. 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.
5.2.4. Abstraction, Aggregation, Reporting
The outcome of Intent Assurance needs to be reported back to the user
in ways that allow 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 human users.
6. Lifecycle
Intent is subject to a lifecycle: it comes into being, may undergo
changes over the course of time, and may at some point be retracted.
This lifecycle 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 5 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:
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
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and that allow users to assess outcomes and 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 the network
operations infrastructure. This includes the functions used to
translate an intent into a course of action as well as the
algorithms that are used to plan and optimize those courses of
action also in consideration of feedback and observations from the
network. It also includes the functions to analyze and aggregate
observations from the network in order to validate compliance with
the intent and to take corrective actions as necessary. In
addition, it includes functions that abstract observations from
the network in ways that relate them to the intent as communicated
by users. This facilitates the reporting functions in the user
space.
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 Lifecycle
When carefully inspecting the diagram, it becomes apparent that the
intent lifecycle, 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 represents closed-loop automation that 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. The loop addresses and counteracts any intent drift that
may be occuring, using observations to assess the degree of the
network's intent compliance and automatically prompting actions to
address any discrepancies. Likewise, the loop allows to assess
the effectiveness of any actions that are taken in order to
continuously learn and improve how intent needs to be rendered in
order to achieve the desired outcomes.
o The "outer" intent control loop extends to the user space. It
includes the user taking action and adjusting their intent based
on observations and feedback from the IBS. Intent is thus
subjected to a lifecycle: It comes into being, may undergo
refinements, modifications, and changes of time, and may at some
point in time even get retracted.
7. Additional Considerations
Given the popularity of the term "intent," it is tempting to broaden
it use to encompass also other related concepts, resulting in
"intent-washing" that paints those concepts in a new light by simply
applying new intent terminology to them. A common example concerns
referring to the northbound interface of SDN controllers as "intent
interface". However, in some cases, this actually makes sense not
just as a marketing ploy but as a way to better relate previously
existing and new concepts.
In that sense and regards to intent, it makes 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.
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A comprehensive set of classifications of different concepts and
categories of intent will be described in a separate document.
8. IANA Considerations
Not applicable
9. 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.
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
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combination of a higher degree of automation and the intrinsic higher
degree of freedom, ambiguity, and implicitly 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 of using the intent formalism (a high-level,
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 a 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.
10. Acknowledgments
We would like to thank the members of the IRTF Network Management
Research Group (NMRG) for many valuable discussions and feedback. In
particular, we would like to acknowledge the feedback and support
from Remi Badonnel, Walter Cerroni, Marinos Charalambides, Luis
Contreras, Jerome Francois, Molka Gharbaoui, Olga Havel, Chen Li,
William Liu, Barbara Martini, Stephen Mwanje, Jeferson Nobre, Haoyu
Song, Peter Szilagyi, and Csaba Vulkan. Of those, we would like to
thank the following persons who went one step further and also
provided reviews of the document: Remi Badonnel, Walter Cerroni,
Jerome Francois, Molka Gharbaoui, Barbara Martini, Stephen Mwanje,
Peter Szilagyi, and Csaba Vulkan.
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11. 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.
[Clemm20] Clemm, A., Faten Zhani, M., and R. Boutaba, "Network
Management 2030: Operations and Control of Network 2030
Services. Journal of Network and Systems Management
(JNSM), Springer, Vol. 28 (4).", October 2020.
[Gharbaoui21]
Gharbaoui, M., Martini, B., and P. Castoldi,
""Implementation of an Intent Layer for SDN-enabled and
QoS-aware Network Slicing", in IEEE 7th Int. Conference on
Network Softwarization (NetSoft), pp 17-23, doi: 10.1109/
NetSoft51509.2021.9492643", June 2021.
[I-D.ietf-teas-ietf-network-slice-definition]
Rokui, R., Homma, S., Makhijani, K., Contreras, L. M., and
J. Tantsura, "Definition of IETF Network Slices", draft-
ietf-teas-ietf-network-slice-definition-01 (work in
progress), February 2021.
[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-09 (work in progress), October 2021.
[IEEE-TITS21]
Garg, S., "Special Issue on Intent-Based Networking for
5G-Envisioned Internet of Connected Vehicles, in IEEE
Transactions on Intelligent Transportation Systems, vol.
22, no. 8, DOI: 10.1109/TITS.2021.3101259", August 2021.
[IEEEXPLORE]
IEEE, "IEEE eXplore, https://ieeexplore.ieee.org/", 2021.
[Lenrow15]
Lenrow, D., "Intent As The Common Interface to Network
Resources, Intent Based Network Summit 2015 ONF Boulder:
IntentNBI", February 2015.
[M3010] ITU-T, "M.3010 : Principles for a telecommunications
management network.", February 2000.
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[MDPI22] Gharbaoui, M., "Special Issue "Intent-Based Software-
Defined Networks", in Computers (MDPI) (to appear)", 2022.
[Pang20] Pang, L., Yang, C., Chen, D., Song, Y., and M. Guizan, "A
Survey on Intent-Driven Networks", in IEEE Access Vol 8
pp. 22862-22873, DOI: 10:110/ACCESS.2020.2969208", 2020.
[RFC3411] Harrington, D., Presuhn, R., and B. Wijnen, "An
Architecture for Describing Simple Network Management
Protocol (SNMP) Management Frameworks", STD 62, RFC 3411,
DOI 10.17487/RFC3411, December 2002,
<https://www.rfc-editor.org/info/rfc3411>.
[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>.
[RFC7950] Bjorklund, M., Ed., "The YANG 1.1 Data Modeling Language",
RFC 7950, DOI 10.17487/RFC7950, August 2016,
<https://www.rfc-editor.org/info/rfc7950>.
[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>.
[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>.
[RFC8994] Eckert, T., Ed., Behringer, M., Ed., and S. Bjarnason, "An
Autonomic Control Plane (ACP)", RFC 8994,
DOI 10.17487/RFC8994, May 2021,
<https://www.rfc-editor.org/info/rfc8994>.
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Authors' Addresses
Alexander Clemm
Futurewei
2330 Central Expressway
Santa Clara, CA 95050
USA
Email: ludwig@clemm.org
Laurent Ciavaglia
Rakuten Mobile
Email: laurent.ciavaglia@rakuten.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
Microsoft
Email: jefftant.ietf@gmail.com
Clemm, et al. Expires June 18, 2022 [Page 28]