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draft-nmrg-ibn-concepts-definitions-00.txt
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Network Working Group A. Clemm
Internet-Draft Futurewei
Intended status: Informational L. Ciavaglia
Expires: June 26, 2020 Nokia
L. Granville
Federal University of Rio Grande do Sul (UFRGS)
J. Tantsura
Apstra, Inc.
December 24, 2019
Intent-Based Networking - Concepts and Definitions
draft-irtf-nmrg-ibn-concepts-definitions-00
Abstract
Intent and Intent-Based Networking 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 is intended to clarify the
concept of "Intent" and provide an overview of functionality that
associated with it. The goal is to contribute towards a common and
shared understanding of terms, concepts, and functionality which can
be used as foundation to guide further definition of associated
research and engineering problems and their solutions.
Status of This Memo
This Internet-Draft is submitted in full conformance with the
provisions of BCP 78 and BCP 79.
Internet-Drafts are working documents of the Internet Engineering
Task Force (IETF). Note that other groups may also distribute
working documents as Internet-Drafts. The list of current Internet-
Drafts is at https://datatracker.ietf.org/drafts/current/.
Internet-Drafts are draft documents valid for a maximum of six months
and may be updated, replaced, or obsoleted by other documents at any
time. It is inappropriate to use Internet-Drafts as reference
material or to cite them other than as "work in progress."
This Internet-Draft will expire on June 26, 2020.
Copyright Notice
Copyright (c) 2019 IETF Trust and the persons identified as the
document authors. All rights reserved.
Clemm, et al. Expires June 26, 2020 [Page 1]
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This document is subject to BCP 78 and the IETF Trust's Legal
Provisions Relating to IETF Documents
(https://trustee.ietf.org/license-info) in effect on the date of
publication of this document. Please review these documents
carefully, as they describe your rights and restrictions with respect
to this document. Code Components extracted from this document must
include Simplified BSD License text as described in Section 4.e of
the Trust Legal Provisions and are provided without warranty as
described in the Simplified BSD License.
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 . . . . . . . . . . . . . . . . . . . . 6
4.2.1. Service Models . . . . . . . . . . . . . . . . . . . 6
4.2.2. Policy and Policy-Based Management . . . . . . . . . 8
4.2.3. Distinguishing between Intent, Policy, and Service
Models . . . . . . . . . . . . . . . . . . . . . . . 10
5. Principles . . . . . . . . . . . . . . . . . . . . . . . . . 11
6. Lifecycle . . . . . . . . . . . . . . . . . . . . . . . . . . 14
7. Intent-Based Networking - Functionality . . . . . . . . . . . 18
7.1. Intent Fulfillment . . . . . . . . . . . . . . . . . . . 18
7.2. Intent Assurance . . . . . . . . . . . . . . . . . . . . 18
8. Items for Discussion . . . . . . . . . . . . . . . . . . . . 19
9. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 19
10. Security Considerations . . . . . . . . . . . . . . . . . . . 19
11. References . . . . . . . . . . . . . . . . . . . . . . . . . 19
11.1. Normative References . . . . . . . . . . . . . . . . . . 19
11.2. Informative References . . . . . . . . . . . . . . . . . 19
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 21
1. Introduction
Traditionally in the IETF, interest with regard to 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
prime example for 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-
Clemm, et al. Expires June 26, 2020 [Page 2]
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by-device basis is no longer sustainable. Big 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. Adoptability to changes at scale is a
fundamental property of a well designed IBN system, that requires
abilty to consume and process analytics that are 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 allow for rapid reconfiguration of networks
at sub-second time scales and to ensure networks are delivering their
functionality as expected.
Accordingly, 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]. In addition, a lot
of 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 management of the network as a whole exceptionally
easy, 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 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 lifecycle of intent),
SDN controllers (offering a single point of control and
administration for a network) as well as 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
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 top-down fashion from
upper to lower layers. The associated abstraction hierarchy was key
Clemm, et al. Expires June 26, 2020 [Page 3]
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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 management" 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 it to account for current technology
trends. This document attempts to clarify 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. In addition, a
number of research challenges are highlighted. 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.
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
Intent: An abstracted, declarative and vendor agnostic set of
rules used to provide full lifecycle (Design/Build/Deploy/
Validate) to a network and services it provides.
Policy: A rule, or set of rules, that governs the choices in
behavior of a system.
SSoT: Single Source of Truth - A functional block in an IBN system
that normalizes user' intent and serves as the single source of
data for the lower layers.
IBA: Intent Based Analytics - Analytics that are defined and
derived from user' intent and used to validate the intended state.
Clemm, et al. Expires June 26, 2020 [Page 4]
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IBS: Intent Based 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.
4. Introduction of Concepts
The following section provides an overview of the concept of intent
respectively intent-based management. It also provides an overview
of the related concepts of service models, and of policies
respectively policy-based management, and explains how they relate to
intent and intent-based management.
4.1. Intent and Intent-Based Management
In the context of Autonomic Networks, Intent 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.
However, to avoid using "intent" simply as a synonym for "policy, a
clearer distinction needs to be introduced that distinguishes intent
clearly from other types of policies.
For one, while Intent-Based Management clearly 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 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.
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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, an
algorithm) for how to achieve the outcome.
In an autonomic network, intent should be rendered by the network
itself, i.e. translated into device-specific rules and courses of
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. Because intent holds for the network as a whole,
not individual devices, it needs to be automatically disseminated
across all devices in the network, which can themselves decide
whether they need to act on it. This facilitates management even
further, since it obviates the need for a higher-layer system to
break down and decompose higher-level intent, and because there is no
need to even discover and maintain an inventory of the network to be
able to manage it.
Tentative definition for intent-based networks Networks configuring
and adapting autonomously to the user or operator intentions (i.e., a
desired state or behavior) without the need to specify every
technical detail of the process and operations to achieve it (i.e.,
the "machines" will figure out on their own how to realize the user
goal).
Other definitions of intent exist such as [TR523] and will be
investigated in future revisions of this document. Likewise, some
definitions of intent allow for the presence of a centralized
function that renders the intent into lower-level policies or
instructions and orchestrates them across the network. While to the
end user the concept of "intent" appears the same regardless of its
method of rendering, this interpretation opens a slippery slope of
how to clearly distinguish "intent" from other higher-layer
abstractions. Again, these notions will be further investigated in
future revisions of this document and in collaboration with NMRG.
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/vendor agnostic way that can
be used independent of the equipment and operating environment on
which the service is realized. Two subcategories are distinguished:
Clemm, et al. Expires June 26, 2020 [Page 6]
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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 lifecycles. 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 [I-D.ietf-teas-te-service-mapping-yang] is an example of such
mapping - a data model to map customer service models (e.g., the
L3VPM Service Model) to Traffic Engineering (TE) models (e.g., the
TE Tunnel or the Abstraction and Control of Traffic Engineered
Networks Virtual Network model)
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. Orchestration itself is generally conducted
using a "push" model, in which the controller/manager initiates the
operations as required, pushing down the specific configurations 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.
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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 catalogues, tariffs, service
contracts, and Service Level Agreements (SLAs) including contractual
agreements regarding remediation actions.
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, management of service models requires development
of sophisticated algorithms and control logic by network providers or
system integrators.
4.2.2. Policy and Policy-Based Management
Policy-based management (PBM) 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 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
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(that get assessed and that 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 specify precisely what needs to be done when and
in which circumstance. 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 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,
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 to apply 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
defintion across different networking domains, WAN, DC or public
cloud.
Policy-based management 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 operation. That 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
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and hence acts as a PEP (Policy Enforcement Point). Either way, PBM
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 specification of rules that allow to define
various triggers for certain 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.
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
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 themselves, without requiring
intervention by outside system. Policy lets users define what to
do under what circumstances, but it does not specify a desired
outcome.
o Intent is a higher-level declarative policy 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 the need to enumerate specific events, conditions,
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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, ideally, intent is
rendered by the network itself; also the dissemination of intent
across the network and any required coordination between nodes is
resolved by the network itself without the need for outside
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. They are able to make
automatic inferences based on those rules, but are not able to
"learn" 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. However, they do require a learning
or training phase and explanations of actions that the system
actually takes provide a different set of challenges.
5. Principles
The following 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 intented
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
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[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 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
interactive 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.
3. Autonomy and Oversight. 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 oversight 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 expiations in terms of performance and quality, while at
the same time providing the proper level of oversight to satisfy
the user requirements for reporting and escalation of relevant
information. to be added: description for feedback, reporting,
guarantee scope (check points, guard rails, dynamically
provisioned, context rich, regular operation vs. exception/
abnormal, information zoom in-out, and link to SVoT. Accountable
for decisions and efficiency, late binding (leave it to the
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system where to place functionality, how to accomplish certain
goals).
4. Learning. An intent-based system is a learning system. By
contrast to imperative type of system, such as Event-Condition-
Action policy rules, where the user define beforehand the
expected behavior of the system to various event and conditions,
in an intent-based system, the user only declare 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
implies also 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. Explainability. Need expressive network capabilities,
requirements and constraints to be able to compose/decompose
intents, map user's expectation to system capabilities.
capability exposure. not just automation of steps that need to
be taken, but of bridging the semantic gap between "intent" and
actionable levels of instructions Context: multi providers, need
discovery and semantic descriptions Explainability: why is a
network doing what it is doing
6. Abstraction - users do not need to be concerned with how intent
is achieved
Additional principles will be described in future revision of this
document addressing aspects such as: Target groups not individual
devices, agnostic to implementation details, user-friendly, user
vocabulary vs. language of the device/network, explainability,
validation and troubleshooting, how to resolve and point out
conflicts (between intents), reconcile the reality of what is
possible with the fiction of what the user would want, "moderate",
awareness of operating within system boundaries, outcome-driven
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((what not how, for the user);(what and how/where, for the
operator).not imperative/instruction based.).
The above principles will be further used to understand implications
on the design of intent-based systems and their supporting
architecture, and derive functional and operational requirements.
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 lifecycle and its main functions. The
functions are divided into two functional (horizontal) planes and
into three (vertical) spaces.
The functional planes provide structure for the main funcional
concerns that are associated with intent: how to fulfill intent, and
how to assure it.
o 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. This
includes:
* Functions that recognize intent from interaction with the user
and functions that allow users to refine their intent and
articulate it in such ways so that it becomes actionable by an
Intent-Based System. Those functions can involve
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 tradeoffs, and to facilitate refinements.
* Functions that translate user intent into courses of actions
and requests to take against the network, which will be
meaningful to network configuration and provisioning systems.
Possibly, this includes learning functions and algorithms that
optimize the courses of actions to take in order to result in
the best outcomes, specifically in cases where multiple ways of
achieving those outcomes are conceivable.
* Functions that perform and orchestrate the configuration and
provisioning steps that were determined by the previous intent
translation step.
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o Assurance is concerned with the functions that are necessary
ensure that the network indeed complies with the desired intent
once it has been fulfilled. This includes:
* Functions that monitor and observe the network and its
exhibited behavior.
* Functions that assess and validate whether the observation
indicate compliance with intent. This can include functions
that perform analysis and aggregation of raw observation data.
* Functions that trigger corrective action as needed.
* Functions that abstract the observations and analysis results
in a way that makes it possible for users to relate them to
intent. 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.
* Functions that report intent compliance status and that provide
adequate summarization and visualization to the user.
The spaces indicate the different perspectives and interactions with
different roles that are involved to address 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
and that allow users to assess whether their intent is having 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 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.
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User Space : Translation / IBS : Network Ops
: Space : Space
: :
+---------+ : +----------+ +-----------+ : +---------+
Fulfill |recognize| ---> |translate/|-->|learn/plan/| ---> | config/ |
|intent | <--- | refine | | render | : |provision|
+----^----+ : +----------+ +-----^-----+ : +---------+
| : | : |
.............|................................|..................|.....
| : +----+---+ : v
| : |validate| : +----------+
| : +----^---+ <------| monitor/ |
Assure +---+---+ : +---------+ +-----+---+ : | observe/ |
|report | <---- |abstract |<---| analyze | <------| assure |
+-------+ : +---------+ |aggregate| : +----------+
: +---------+ :
Figure 1: Intent Lifecycle
When inspecting the diagram carefully, it become apparent that the
intent lifecycle in fact involves two cycles, or loops:
o The "inner" intent control loop between IBS and Network Operations
space is completely automated 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, and
feeding those observations 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 also the user space and
includes the user taking action and adjusting their intent based
on feedback from the IBS.
Slight alternatives in intent lifecycles and the functions involved
are conceivable. Figure 2 depicts one such alternative with an
emphasis in intent fulfilment. (Todo: Intent attributes, intent
states. Distinguish flow from users to network, and from network to
user.)
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user related user data <-----<-+--------+
data + + | |
| | | |
+----v------+ +-----v-----+ | |
| recognize +---+ +-----+ generate | | |
user +-----------+ | | +-----------+ | |
space | | | |
+--------------------------------------------------------------------+
system | | | |
space +---v---v---+ +----------+ +-----+-----+ |
| translate <-->+ validate <---> recommend | |
+-----+-----+ +----------+ +-----------+ |
| |
+-----v-----+ |
| normalize | |
+-----+-----+ |
| |
+-----v-----+ |
| decompose | |
+-----+-----+ |
| |
+------v------+ |
| communicate | |
+------+------+ |
preparation | |
phase | |
+-------------------------------------------------------------------+
operation | |
phase +-----v----+ |
| fullfill | |
+-----+----+ |
| |
+----v----+ +--------+ |
| observe +-----> report +-------------------+
+----+----+ +--------+
|
+----v---+
| assure |
+--------+
Figure 2: Intent Lifecycle (alt.)
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7. 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 orchestrates
the intent, i.e. that breaks down intent abstractions into lower-
level network and device abstractions and performs or coordinates
the configuration operations across the network.
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. Control plane or lower-level
management operations can cause behavior that inadvertently
conflicts with intent which was orchestrated earlier.
Accordingly, "intent drift" may occur. Network operators need to
be able to detect when such drift occurs, or is about to occur,
and be provided with the necessary functions to resolve such
conflicts. This can occur by either bringing the network back
into compliance, or by articulating modifications to the original
intent to moderate between conflicting interests.
The following sections provide a more comprehensive overview of those
functions.
7.1. Intent Fulfillment
RBD
7.2. Intent Assurance
Ability to reason about system' state by employing closed-loop
validation in the presence of an inevitable change is a fundamental
property of an Intent Assurance part of an IBN system. Since service
expectations are created during intent consumption and modeling
phase, closed-loop intent vaidation should start immidiatelly, with
the service instantiation. Telemetry consumed could then be enriched
with an additional context and must always be processed in context of
the Intent it has been instantiated. Direct relationship between the
Intent and telemetry gathered enables correlation between changes in
states and the Intent and provides contextual base for reasoning
about the changes.