draft-nmrg-ibn-concepts-definitions.txt

Network Working Group                                           A. Clemm
Internet-Draft                                                 Futurewei
Intended status: Informational                              L. Ciavaglia
Expires: September 12, 2020                                        Nokia
                                                            L. Granville
                         Federal University of Rio Grande do Sul (UFRGS)
                                                             J. Tantsura
                                                            Apstra, Inc.
                                                          March 11, 2020


           Intent-Based Networking - Concepts and Definitions
              draft-irtf-nmrg-ibn-concepts-definitions-01

Abstract

   Intent and Intent-Based Networking (IBN) are taking the industry by
   storm.  At the same time, those terms are used loosely and often
   inconsistently, in many cases overlapping and confused with other
   concepts such as "Policy".  This document clarifies the concept of
   "Intent" and provides an overview of functionality that is associated
   with it.  The goal is to contribute towards a common and shared
   understanding of terms, concepts, and functionality that can be used
   as foundation to guide further definition of associated research and
   engineering problems and their solutions.

Status of This Memo

   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
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   This Internet-Draft will expire on September 12, 2020.

Copyright Notice

   Copyright (c) 2020 IETF Trust and the persons identified as the
   document authors.  All rights reserved.




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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
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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
   2.  Key Words . . . . . . . . . . . . . . . . . . . . . . . . . .   4
   3.  Definitions and Acronyms  . . . . . . . . . . . . . . . . . .   4
   4.  Introduction of Concepts  . . . . . . . . . . . . . . . . . .   5
     4.1.  Intent and Intent-Based Management  . . . . . . . . . . .   5
     4.2.  Related Concepts  . . . . . . . . . . . . . . . . . . . .   6
       4.2.1.  Service Models  . . . . . . . . . . . . . . . . . . .   7
       4.2.2.  Policy and Policy-Based Network Management  . . . . .   8
       4.2.3.  Distinguishing between Intent, Policy, and Service
               Models  . . . . . . . . . . . . . . . . . . . . . . .  10
   5.  Principles  . . . . . . . . . . . . . . . . . . . . . . . . .  11
   6.  Intent-Based Networking - Functionality . . . . . . . . . . .  14
     6.1.  Intent Fulfillment  . . . . . . . . . . . . . . . . . . .  14
     6.2.  Intent Assurance  . . . . . . . . . . . . . . . . . . . .  15
   7.  Life-cycle  . . . . . . . . . . . . . . . . . . . . . . . . .  16
   8.  Items for Discussion  . . . . . . . . . . . . . . . . . . . .  17
   9.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .  18
   10. Security Considerations . . . . . . . . . . . . . . . . . . .  18
   11. References  . . . . . . . . . . . . . . . . . . . . . . . . .  19
     11.1.  Normative References . . . . . . . . . . . . . . . . . .  19
     11.2.  Informative References . . . . . . . . . . . . . . . . .  20
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  21

1.  Introduction

   Traditionally in the IETF, interest regarding management and
   operations has focused on individual network and device features.
   Standardization emphasis has generally been put on management
   instrumentation that needed to be provided to a networking device.  A
   prime example of this is SNMP-based management and the 200+ MIBs that
   have been defined by the IETF over the years.  More recent examples
   include YANG data model definitions for aspects such as interface
   configuration, ACL configuration, or Syslog configuration.

   There is a sense and reality that in modern network environments
   managing networks by configuring myriads of "nerd knobs" on a device-



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   by-device basis is no longer sustainable.  Significant challenges
   arise with keeping device configurations not only consistent across a
   network, but consistent with the needs of services and service
   features they are supposed to enable.  Adaptability to changes at
   scale is a fundamental property of a well-designed IBN system, that
   requires the ability to consume and process analytics that is
   context/intent aware at near real-time speeds.  At the same time,
   operations need to be streamlined and automated wherever possible to
   not only lower operational expenses, but also allow for rapid
   reconfiguration of networks at sub-second time scales and to ensure
   that networks are delivering their functionality as expected.

   Accordingly, the IETF has begun to address end-to-end management
   aspects that go beyond the realm of individual devices in isolation.
   Examples include the definition of YANG models for network topology
   [RFC8345] or the introduction of service models used by service
   orchestration systems and controllers [RFC8309].  Much interest has
   been fueled by the discussion about how to manage autonomic networks,
   as discussed in the ANIMA working group.  Autonomic networks are
   driven by the desire to lower operational expenses and make the
   management of the network as a whole more straightforward, putting it
   at odds with the need to manage the network one device and one
   feature at a time.  However, while autonomic networks are intended to
   exhibit "self-management" properties, they still require input from
   an operator or outside system to provide operational guidance and
   information about the goals, purposes, and service instances that the
   network is to serve.

   This vision has since caught on with the industry in a big way,
   leading to a significant number of solutions that offer "Intent-based
   management" that promise network providers to manage networks
   holistically at a higher level of abstraction and as a system that
   happens to consist of interconnected components, as opposed to a set
   of independent devices (that happen to be interconnected).  Those
   offerings include IBN systems (offering full a life-cycle of intent),
   SDN controllers (offering a single point of control and
   administration for a network), and network management and Operations
   Support Systems (OSS).

   However, it has been recognized for a long time that comprehensive
   management solutions cannot operate only at the level of individual
   devices and low-level configurations.  In this sense, the vision of
   "Intent" is not entirely new.  In the past, ITU-T's model of a
   Telecommunications Management Network, TMN, introduced a set of
   management layers that defined a management hierarchy, consisting of
   network element, network, service, and business management.  High-
   level operational objectives would propagate in a top-down fashion
   from upper to lower layers.  The associated abstraction hierarchy was



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   crucial to decompose management complexity into separate areas of
   concerns.  This abstraction hierarchy was accompanied by an
   information hierarchy that concerned itself at the lowest level with
   device-specific information, but that would, at higher layers,
   include, for example, end-to-end service instances.  Similarly, the
   concept of "Policy-based Network Management (PBNM)" has, for a long
   time, touted the ability to allow users to manage networks by
   specifying high-level management policies, with policy systems
   automatically "rendering" those policies, i.e., breaking them down
   into low-level configurations and control logic.

   What has been missing, however, is putting these concepts into a more
   current context and updating them to account for current technology
   trends.  This document clarifies the concepts behind intent.  It
   differentiates it from related concepts.  It also provides an
   overview of first-order principles of Intent-Based Networking as well
   as associated functionality.  The goal is to contribute to a common
   and shared understanding that can be used as a foundation to
   articulate research and engineering problems in the area of Intent-
   Based Networking.

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: 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.

      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.




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      Policy: A set of rules that governs the choices in behavior of a
      system.

      PDP: Policy Decision Point.

      PEP: Policy Enforcement Point.

      Service Model: A model that represents a service that is provided
      by a network to a user.

      SSoT: Single Source of Truth - A functional block in an IBN system
      that normalizes users' intent and serves as the single source of
      data for the lower layers.

4.  Introduction of Concepts

   The following section provides an overview of the concept of Intent
   and Intent-Based Management.  It also provides an overview of the
   related concepts of service models, and of policies respectively
   Policy-Based Network Management, and explains how they relate to
   Intent and Intent-Based Management.

4.1.  Intent and Intent-Based Management

   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
   distinction needs to be introduced that differentiates Intent clearly
   from other types of policies.

   For one, while Intent-Based Management aims to lead towards networks
   that are dramatically simpler to manage and operate requiring only
   minimal outside intervention, the concept of "Intent" is not limited
   to autonomic networks, but applies to any network.  Networks, even
   when considered "autonomic", are not clairvoyant and have no way of
   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:



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   o  It provides data abstraction: Users and operators do not need to
      be concerned with low-level device configuration and nerd knobs.

   o  It provides functional abstraction from particular management and
      control logic: Users and operators do not need to be concerned
      even with how to achieve a given Intent.  What is specified is a
      desired outcome, with the Intent-based system automatically
      figuring out a course of action (e.g., a set of rules (thus, a set
      of rules is not part of an intent but rather derived from that
      intent), an algorithm) for how to achieve the outcome.

   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 even to 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







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4.2.1.  Service Models

   A service model is a model that represents a service that is provided
   by a network to a user.  Per [RFC8309], a service model describes a
   service and its parameters in a portable/implementation-agnostic way
   that can be used independently of the equipment and operating
   environment on which the service is realized.  Two subcategories are
   distinguished: a "Customer Service Model" describes an instance of a
   service as provided to a customer, possibly associated with a service
   order.  A "Service Delivery Model" describes how a service is
   instantiated over existing networking infrastructure.

   An example of a service could be a Layer 3 VPN service [RFC8299], a
   Network Slice, or residential Internet access.  Service models
   represent service instances as entities in their own right.  Services
   have their own parameters, actions, and life-cycles.  Typically,
   service instances can be bound to end-users, who might be billed for
   the service.

   Instantiating a service typically involves multiple aspects:

   o  A user (or northbound system) needs to define and/or request a
      service to be instantiated.

   o  Resources need to be allocated, such as IP addresses, AS numbers,
      VLAN or VxLAN pools, interfaces, bandwidth, or memory.

   o  How to map services to the resources needs to be defined.
      Multiple mappings are often possible, which to select may depend
      on context (such as which type of access is available to connect
      the end user with the service).

   o  Bindings need to be maintained between upper and lower-level
      objects.

   o  Once instantiated, the service needs to be validated and assured
      to ensure that the network indeed delivers the service as
      requested.

   They involve a system, such as a controller, that provides
   provisioning logic.  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 catalogs, tariffs, service
   contracts, and Service Level Agreements (SLAs), including contractual
   agreements regarding remediation actions.

   [I-D.ietf-teas-te-service-mapping-yang] is an example of a data model
   that provides a mapping for customer service models (e.g., the L3VPN
   Service Model) to Traffic Engineering (TE) models (e.g., the TE
   Tunnel or the Abstraction and Control of Traffic Engineered Networks
   Virtual Network model)

   Like intent, service models provide higher layers of abstraction.
   Service models are often also complemented with mappings that capture
   dependencies between service and device or network configurations.
   Unlike intent, service models do not allow to define a desired
   "outcome" that would be automatically maintained by the intent
   system.  Instead, the management of service models requires the
   development of sophisticated algorithms and control logic by network
   providers or system integrators.

4.2.2.  Policy and Policy-Based Network Management

   Policy-Based Network Management (PBNM) is a management paradigm that
   separates the rules that govern the behavior of a system from the
   functionality of the system.  It promises to reduce maintenance costs
   of information and communication systems while improving flexibility
   and runtime adaptability.  It is present today at the heart of a
   multitude of management architectures and paradigms, including SLA-
   driven, Business-driven, autonomous, adaptive, and self-* management
   [Boutaba07].  The interested reader is asked to refer to the rich set
   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



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   the choices in the behavior of a system" [Sloman94].  "Policy is a
   set of rules that are used to manage and control the changing and/or
   maintaining of the state of one or more managed objects"
   [Strassner03].  Common to most definitions is the definition of a
   policy as a "rule".  Typically, the definition of a rule consists of
   an event (whose occurrence triggers a rule), a set of conditions
   (which get assessed and which must be true before any actions are
   actually "fired"), and finally a set of one or more actions that are
   carried out when the condition holds.

   Policy-based management can be considered an imperative management
   paradigm: Policies precisely specified what needs to be done when and
   in which circumstance.  By using policies, management can, in effect,
   be defined as a set of simple control loops.  This makes policy-based
   management a suitable technology to implement autonomic behavior that
   can exhibit self-* management properties, including self-
   configuration, self-healing, self-optimization, and self-protection.
   In effect, policies define management as a set of simple control
   loops.

   Policies typically involve a certain degree of abstraction in order
   to cope with the heterogeneity of networking devices.  Rather than
   having a device-specific policy that defines events, conditions, and
   actions in terms of device-specific commands, parameters, and data
   models, a policy is defined at a higher-level of abstraction
   involving a canonical model of systems and devices to which the
   policy is to be applied.  A policy agent on a controller or the
   device subsequently "renders" the policy, i.e., translates the
   canonical model into a device-specific representation.  This concept
   allows applying the same policy across a wide range of devices
   without needing to define multiple variants.  In other words - policy
   definition is de-coupled from policy instantiation and policy
   enforcement.  This enables operational scale and allows network
   operators and authors of policies to think in higher terms of
   abstraction than device specifics and be able to reuse the same,
   high-level definition across different networking domains, WAN, DC,
   or public cloud.

   PBNM is typically "push-based": Policies are pushed onto devices
   where they are rendered and enforced.  The push operations are
   conducted by a manager or controller, which is responsible for
   deploying policies across the network and monitor their proper
   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



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   a network device observes an event that is associated with a policy,
   but lacks the full definition of the policy or the ability to reach a
   conclusion regarding the expected action, it reaches out to the PDP
   for a decision (reached, for example, by deciding on an action based
   on various conditions).  Subsequently, the device carries out the
   decision as returned by the PDP - the device "enforces" the policy
   and hence acts as a PEP (Policy Enforcement Point).  Either way, PBNM
   architectures typically involve a central component from which
   policies are deployed across the network, and/or policy decisions
   served.

   Like Intent, policies provide a higher layer of abstraction.  Policy
   systems are also able to capture dynamic aspects of the system under
   management through the specification of rules that allow defining
   various triggers for specific courses of actions.  Unlike intent, the
   definition of those rules (and courses of actions) still needs to be
   articulated by users.  Since the intent is unknown, conflict
   resolution within or between policies requires interactions with a
   user or some kind of logic that resides outside of PBM.

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, without requiring intervention by
      the outside system.  Policy lets users define what to do under
      what circumstances, but it does not specify the desired outcome.



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   o  Intent is a high-level, declarative goal that operates at the
      level of a network and services it provides, not individual
      devices.  It is used to define outcomes and high-level operational
      goals, without specifying how those outcomes and should be
      achieved or how goals should specifically be satisfied, and
      without the need to enumerate specific events, conditions, and
      actions.  Which algorithm or rules to apply can be automatically
      "learned/derived from intent" by the intent system.  In the
      context of autonomic networking, intent is ideally rendered by the
      network itself; also, the dissemination of intent across the
      network and any required coordination between nodes is resolved by
      the network without the need for external systems.

   One analogy to capture the difference between policy and intent
   systems is that of Expert Systems and Learning Systems in the field
   of Artificial Intelligence.  Expert Systems operate on knowledge
   bases with rules that are supplied by users.  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 or articulation of rules.  However,
   they do require a learning or training phase, and explanations of
   actions that the system actually takes provide a different set of
   challenges.

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 intended
       state and actual state of the system and determining drift
       between them.  SSoT and the drift information provide the basis
       for corrective actions.  If the intent-based is equipped with
       prediction capabilities or means, it can further develop
       strategies to anticipate, plan, and pro-actively act on the
       diverging trends with the aim to minimize their impact.  Beyond
       providing a means for consistent system operation, SSoT also
       allows for better traceability to validate if/how the initial
       intent and associated business goals have been properly met, to
       evaluate the impacts of changes in the intent parameters and
       impacts and effects of the events occurring in the system.
       Single Version (or View) of Truth derives from the SSoT and can
       be used to perform other operations such as query, poll, or



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       filter the measured and correlated information to create so-
       called "views".  These views can serve the operators and/or the
       users of the intent-based system.  To create intents as single
       sources of truth, the intent-based system must follow well-
       specified and well-documented processes and models.  In other
       contexts [Lenrow15], SSoT is also referred to as the invariance
       of the intent.

   2.  One-touch but not one-shot.  In an ideal intent-based system, the
       user expresses its intents in one form or another, and then the
       system takes over all subsequent operations (one-touch).  A zero-
       touch approach could also be imagined in the case where the
       intent-based system has the capabilities or means to recognize
       intentions in any form of data.  However, the zero- or one-touch
       approach should not be mistaken the fact that reaching the state
       of a well-formed and valid intent expression is not a one-shot
       process.  On the contrary, the interfacing between the user and
       the intent-based system could be designed as an interactive and
       iterative process.  Depending on the level of abstraction, the
       intent expressions will initially contain more or less implicit
       parts, and unprecise or unknown parameters and constraints.  The
       role of the intent-based system is to parse, understand, and
       refine the intent expression to reach a well-formed and valid
       intent expression that can be further used by the system for the
       fulfillment and assurance operations.  An intent refinement
       process could use a combination of iterative steps involving the
       user to validate the proposed refined intent and to ask the user
       for clarifications in case some parameters or variables could not
       be deduced or learned by the means of the system itself.  In
       addition, the Intent-Based System will need to moderate between
       conflicting intent, helping users to properly choose between
       intent alternatives that may have different ramifications.

   3.  Autonomy and Supervision.  A desirable goal for an intent-based
       system is to offer a high degree of flexibility and freedom on
       both the user side and system side, e.g., by giving the user the
       ability to express intents using its own terms, by supporting
       different forms of expression of intents and being capable of
       refining the intent expressions to well-formed and exploitable
       expressions.  The dual principle of autonomy and supervision
       allows to operate a system that will have the necessary levels of
       autonomy to conduct its tasks and operations without requiring
       intervention of the user and taking its own decisions (within its
       areas of concern and span of control) as how to perform and meet
       the user expectations in terms of performance and quality, while
       at the same time providing the proper level of supervision to
       satisfy the user requirements for reporting and escalation of
       relevant information.



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   4.  Learning.  An intent-based system is a learning system.  By
       contrast to the imperative type of system, such as Event-
       Condition-Action policy rules, where the user defines beforehand
       the expected behavior of the system to various events and
       conditions, in an intent-based system, the user only declares
       what the system should achieve and not how to achieve these
       goals.  There is thus a transfer of reasoning/rationality from
       the human (domain knowledge) to the system.  This transfer of
       cognitive capability also implies the availability in the intent-
       based system of capabilities or means for learning, reasoning,
       and knowledge representation and management.  The learning
       abilities of an intent-based systems can apply to different tasks
       such as optimization of the intent rendering or intent refinement
       processes.  The fact that an intent-based system is a
       continuously evolving system creates the condition for continuous
       learning and optimization.  Other cognitive capabilities such as
       planning can also be leveraged in an intent-based system to
       anticipate or forecast future system state and response to
       changes in intents or network conditions and thus elaboration of
       plans to accommodate the changes while preserving system
       stability and efficiency in a trade-off with cost and robustness
       of operations.  Cope with unawareness of users (smart
       recommendations).

   5.  Capability exposure.  Capability exposure consists in the need
       for expressive network capabilities, requirements, and
       constraints to be able to compose/decompose intents and map the
       user's expectations to the system capabilities.

   6.  Abstraction.  Users do not need to be concerned with how intent
       is achieved and are empowered to think in terms of outcomes.  In
       addition, they do can refer to concepts at a higher level of
       abstractions, independent e.g. of vendor-specific renderings.

   Additional principles will be described in future revision of this
   document addressing aspects such as: Intent target being groups of
   devices and 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 ((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.



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6.  Intent-Based Networking - Functionality

   Intent-Based Networking involves a wide variety of functions which
   can be roughly divided into two categories:

   o  Intent Fulfillment provides functions and interfaces that allow
      users to communicate intent to the network, and that perform the
      necessary actions to ensure that intent is achieved.  This
      includes algorithms to determine proper courses of action and
      functions that learn to optimize outcomes over time.  In addition,
      it also includes more traditional functions such as any required
      orchestration of coordinated configuration operations across the
      network and rendering of higher-level abstractions into lower-
      level parameters and control knobs.

   o  Intent Assurance provides functions and interfaces that allow
      users to validate and monitor that the network is indeed adhering
      to and complying with intent.  This is necessary to assess the
      effectiveness of actions taken as part of fulfillment, providing
      important feedback that allows those functions to be trained or
      tuned over time to optimize outcomes.  In addition, Intent
      Assurance is necessary to address "intent drift".  Intent drift
      occurs when a system originally meets the intent, but over time
      gradually allows its behavior to change or be affected until it no
      longer does, or does so in a less effective manner.

   The following sections provide a more comprehensive overview of those
   functions.

6.1.  Intent Fulfillment

   Intent fulfillment is concerned with the functions that take intent
   from its origination by a user (generally, an administrator of the
   responsible organization) to its realization in the network.  This
   includes:

   o  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 trade-offs, and to
      facilitate refinements.

   o  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.  As an



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      option,those functions can be complemented with functions and
      algorithms that optimize the courses of actions 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.

   o  Functions that perform and orchestrate the configuration and
      provisioning steps that were determined by the previous intent
      translation step.

6.2.  Intent Assurance

   Assurance is concerned with the functions that are necessary to
   ensure that the network indeed complies with the desired intent once
   it has been fulfilled.  This includes:

   o  Functions that monitor and observe the network and its exhibited
      behavior.

   o  Functions that assess and validate whether the observation
      indicates compliance with intent, and that recognize intent drift.
      This can include functions that perform analysis and aggregation
      of raw observation data.


      Intent drift can be caused by control plane or lower-level
      management operations that inadvertently cause behavior changes
      which conflict with intent which was orchestrated earlier.
      Intent-Based Systems and Networks need to be able to detect when
      such drift occurs or is about to occur.

   o  Functions that trigger corrective action as 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.

   o  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.

   o  Functions that report intent compliance status and that provide
      adequate summarization and visualization to the user.



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7.  Life-cycle

   Intent is subject to a life-cycle: it comes into being, may undergo
   changes over the course of time, and may at some point be retracted.
   This life-cycle is closely tied to various interconnection functions
   that are associated with the intent concept.

   Figure 1 depicts an intent life-cycle and its main functions.  The
   functions were introduced in Section 6 and are divided into two
   functional (horizontal) planes, reflecting the distinction between
   fulfillment and assurance.  In addition, they are divided into three
   (vertical) spaces.

   The spaces indicate the different perspectives and interactions with
   different roles that are involved in addressing the functions:

   o  The user space involves the functions that interface the network
      and intent-based system with the human user.  It involves the
      functions that allow users to articulate and the intent-based
      system to recognize that intent.  It also involves the functions
      that report back the status of the network relative to the intent
      and that allow users to assess whether their intent has the
      desired effect.

   o  The translation or Intent-Based System (IBS) space involves the
      functions that bridge the gap between intent users and network
      operations.  This includes the functions used to translate an
      intent into a course of action, the algorithms used to plan and
      optimize those courses of actions also in consideration of
      feedback, the functions to analyze and abstract observations to
      validate compliance with the intent and take corrective actions as
      necessary.

   o  The Network Operations space, finally, involves the traditional
      orchestration, configuration, monitoring, and measurement
      functions, which are used to effectuate the rendered intent and
      observe its effects on the network.














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         User Space   :       Translation / IBS        :  Network Ops
                      :            Space               :     Space
                      :                                :
        +----------+   :  +----------+   +-----------+  :  +-----------+
Fulfill |recognize/| ---> |translate/|-->|  learn/   | --> | configure/|
        |generate  |      |          |   |  plan/    |     | provision |
        |intent    | <--- |  refine  |   |  render   |  :  |           |
        +----^-----+   :  +----------+   +-----^-----+  :  +-----------+
             |        :                       |        :        |
.............|................................|.................|....
             |        :                  +----+---+    :        v
             |        :                  |validate|    : +----------+
             |        :                  +----^---+ <----| monitor/ |
Assure   +---+---+    :  +---------+    +-----+---+    : | observe/ |
         |report | <---- |abstract |<---| analyze | <----+----------+
         +-------+    :  +---------+    |aggregate|    :
                      :                 +---------+    :


                        Figure 1: Intent Life-cycle

   When carefully inspecting the diagram, it becomes apparent that the
   intent life-cycle, in fact, involves two cycles, or loops:

   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.  Those
      observations are fed into the function that plans the rendering of
      networking intent in order to make adjustments as needed in the
      configuration of the network.

   o  The "outer" intent control loop involves the user space and
      includes the user taking action and adjusting their intent based
      on feedback from the IBS.

8.  Items for Discussion

   Arguably, given the popularity of the term "intent", its use could be
   broadened to encompass also known concepts ("intent-washing").  For
   example, it is conceivable to introduce intent-based terms for
   various concepts that, although already known, are related to the
   context of intent.  Each of those terms could then designate an
   intent subcategory, for example:

   o  Operational Intent: defines intent related to operational goals of
      an operator; corresponds to the original "intent" term.




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   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.

   Whether to do so is an item for discussion by the Research Group.

9.  IANA Considerations

   Not applicable

10.  Security Considerations

   This document describes concepts and definitions of Intent-based
   Networking.  As such, the below security considerations remain high
   level, i.e. in the form of principles, guidelines or requirements.
   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.





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   Mitigating the effects of erroneous or compromised intents aims at
   making the intent-based system operationally safe by providing
   checkpoint and safeguard mechanisms and operating principles.  In the
   context of Intent-based Networking, such mechanisms and principles
   may consist in the ability to automatically detect unintended,
   detrimental or abnormal behavior; the ability to automatically (and
   gracefully) rollback or fallback to a previous "safe" state; the
   ability to prevent or contain error amplification (due to the
   combination of higher degree of automation and the intrinsic higher
   degree of freedom, ambiguity, and implicit conveyed by intents);
   dynamic levels of supervision and reporting to make the user aware of
   the right information, at the right time with the right level of
   context.  Erroneous or harmful intents may inadvertently propagate
   and compromise security.  In addition, compromised intents, for
   example intent forged by an inside attacker, may sabotage or harm the
   network resources and make them vulnerable to further, larger
   attacks, e.g. by defeating certain security mechanisms.

   Expressing security policies or security-related parameters with
   intents consists in using the intent formalism (a high-level,
   declarative abstraction), or part(s) of an intent statement to define
   security-related aspects such as data governance, level(s) of
   confidentiality in data exchange, level(s) of availability of system
   resources, of protection in forwarding paths, of isolation in
   processing functions, level(s) of encryption, authorized entities for
   given operations, etc.

   The development and introduction of Intent-Based Networking in
   operational environments will certainly create new security concerns.
   Such security concerns have to be anticipated at the design and
   specification time.  However, Intent-Based Networking may also be
   used as an enabler for better security.  For instance, security and
   privacy rules could be expressed in more human-friendly and generic
   way and be less technology-specific and less complex, leading to
   fewer low-level configuration mistakes.  The detection of threats or
   attacks could also be made more simple and comprehensive thanks to
   conflict detection at higher-level or at coarser granularity

   More thorough security analyses should be conducted as our
   understanding of Intent-Based Networking technology matures.

11.  References

11.1.  Normative References







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   [RFC2119]  Bradner, S., "Key words for use in RFCs to Indicate
              Requirement Levels", BCP 14, RFC 2119,
              DOI 10.17487/RFC2119, March 1997,
              <https://www.rfc-editor.org/info/rfc2119>.

   [RFC8174]  Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC
              2119 Key Words", BCP 14, RFC 8174, DOI 10.17487/RFC8174,
              May 2017, <https://www.rfc-editor.org/info/rfc8174>.

11.2.  Informative References

   [Boutaba07]
              Boutaba, R. and I. Aib, "Policy-Based Management: A
              Historical perspective. Journal of Network and Systems
              Management (JNSM), Springer, Vol. 15 (4).", December 2007.

   [I-D.ietf-teas-te-service-mapping-yang]
              Lee, Y., Dhody, D., Fioccola, G., WU, Q., Ceccarelli, D.,
              and J. Tantsura, "Traffic Engineering (TE) and Service
              Mapping Yang Model", draft-ietf-teas-te-service-mapping-
              yang-03 (work in progress), March 2020.

   [Lenrow15]
              Lenrow, D., "Intent As The Common Interface to Network
              Resources, Intent Based Network Summit 2015 ONF Boulder:
              IntentNBI", February 2015.

   [RFC7575]  Behringer, M., Pritikin, M., Bjarnason, S., Clemm, A.,
              Carpenter, B., Jiang, S., and L. Ciavaglia, "Autonomic
              Networking: Definitions and Design Goals", RFC 7575,
              DOI 10.17487/RFC7575, June 2015,
              <https://www.rfc-editor.org/info/rfc7575>.

   [RFC8299]  Wu, Q., Ed., Litkowski, S., Tomotaki, L., and K. Ogaki,
              "YANG Data Model for L3VPN Service Delivery", RFC 8299,
              DOI 10.17487/RFC8299, January 2018,
              <https://www.rfc-editor.org/info/rfc8299>.

   [RFC8309]  Wu, Q., Liu, W., and A. Farrel, "Service Models
              Explained", RFC 8309, DOI 10.17487/RFC8309, January 2018,
              <https://www.rfc-editor.org/info/rfc8309>.

   [RFC8345]  Clemm, A., Medved, J., Varga, R., Bahadur, N.,
              Ananthakrishnan, H., and X. Liu, "A YANG Data Model for
              Network Topologies", RFC 8345, DOI 10.17487/RFC8345, March
              2018, <https://www.rfc-editor.org/info/rfc8345>.





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   [Sloman94]
              Sloman, M., "Policy Driven Management for Distributed
              Systems. Journal of Network and Systems Management (JNSM),
              Springer, Vol. 2 (4).", December 1994.

   [Strassner03]
              Strassner, J., "Policy-Based Network Management.
              Elsevier.", 2003.

   [TR523]    Foundation, O. N., "Intent NBI - Definition and
              Principles. ONF TR-523.", October 2016.

Authors' Addresses

   Alexander Clemm
   Futurewei
   2330 Central Expressway
   Santa Clara,  CA 95050
   USA

   Email: ludwig@clemm.org


   Laurent Ciavaglia
   Nokia
   Route de Villejust
   Nozay  91460
   FR

   Email: laurent.ciavaglia@nokia.com


   Lisandro Zambenedetti Granville
   Federal University of Rio Grande do Sul (UFRGS)
   Av. Bento Goncalves
   Porto Alegre  9500
   BR

   Email: granville@inf.ufrgs.br


   Jeff Tantsura
   Apstra, Inc.

   Email: jefftant.ietf@gmail.com






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