Network Working Group                                           A. Clemm
Internet-Draft                              Futurewei Technologies, Inc.
Intended status: Informational                              L. Ciavaglia
Expires: May 3, 2018                                               Nokia
                                                            L. Granville
                         Federal University of Rio Grande do Sul (UFRGS)
                                                        October 30, 2017

           Distinguishing Intent, Policy, and Service Models


   This document presents existing definitions of the Intent, Policy,
   and Service Models concepts, analyses their differences and
   commonalities, and how the concepts relate to one another.  The
   document is intended to clarify the different concepts and converge
   towards a common and shared understanding, and then use this
   foundation to guide further definition of valid research and
   engineering problems and their solutions.

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   carefully, as they describe your rights and restrictions with respect
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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
   2.  Key Words . . . . . . . . . . . . . . . . . . . . . . . . . .   4
   3.  Definitions and Acronyms  . . . . . . . . . . . . . . . . . .   5
   4.  Introduction of Concepts  . . . . . . . . . . . . . . . . . .   5
     4.1.  Service Models  . . . . . . . . . . . . . . . . . . . . .   5
     4.2.  Policy and Policy-Based Management  . . . . . . . . . . .   6
     4.3.  Intent and Intent-Based Management  . . . . . . . . . . .   7
   5.  Distinguishing between Intent, Policy, and Service Models . .   9
   6.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .   9
   7.  Security Considerations . . . . . . . . . . . . . . . . . . .   9
   8.  References  . . . . . . . . . . . . . . . . . . . . . . . . .   9
     8.1.  Normative References  . . . . . . . . . . . . . . . . . .   9
     8.2.  Informative References  . . . . . . . . . . . . . . . . .  10
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  10

1.  Introduction

   Traditionally in the IETF, interest with regard to management and
   operations has focused on the individual network features and
   devices.  The emphasis has generally been on aspects that needed to
   be provided by 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 NETCONF, RESTCONF,
   and YANG data model definitions including aspects such as interface
   configuration, ACL configuration, or Syslog configuration.  However,
   recent years have seen an increased interest in addressing also upper
   layers of management and managing end-to-end concepts.  Examples
   include the definition of YANG models for network topology, or the
   explanation of service models in the context of service orchestration
   and controllers.  In addition, this 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 need to
   lower operational expenses and make management exceptionally easy,
   putting it at odds with the need to manage the network one device and
   one feature at a time.

   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.  ITU-T's TMN model introduced a set of
   management layers as part of the TMN pyramid, consisting of network

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   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 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.

   Accordingly, there is generally a recognition that to be able to
   manage networks efficiently end-to-end, management needs to be
   applied at higher levels of abstraction than that of low-level device
   details.  Instead, it is required to take a more holistic view, in
   which device-specific details and data models are low-level artifacts
   that should ideally be derived from higher-level concepts.  This can
   be achieved, for example, by higher-level systems that break down
   higher-level concepts (such as an instance of a service) into
   specific device configurations.  Examples of such systems include SDN
   controllers or service provisioning systems.  Potentially, this can
   be even done by intelligent devices themselves, for example, in case
   of autonomic nodes in an autonomic network.  The goal here is that
   ultimately nodes are able to understand high-level concepts and
   automatically coordinate with other nodes to achieve the desired end-
   to-end behavior, without need for intermediate systems.  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.

   In the case of autonomic networks, the high-level guidance given to
   the network is commonly referred to as "Intent" [RFC7575].  The idea
   behind Intent is that a user provides guidance to the network, for
   example, communicate expectations regarding service level objectives
   and service instances or regarding operational goals, such as whether
   to optimize utilization or service levels in a certain operational
   context.  At the same time, the user should have neither to revert to
   low-level configurations nor ideally have to learn a specific
   language of the network.  Instead, the user should be able to simply
   express what the network should accomplish - to convey the user's
   intent.  Ideally, the network would be able to infer the intent using
   a very high-level, natural language or conversational user interface.
   The autonomic network would be able to interpret this intent and
   break it down into low-level configurations as needed, even
   automatically coordinating between nodes to negotiate and tune
   behaviors.  Intent would be conveyed to the autonomic network as a
   whole; propagation of intent among devices would occur automatically,
   as the user should not be concerned with individual nodes nor the
   instantiation of intent across these nodes.

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   The vision of giving the user the ability to communicate to the
   network in very simple, high-level terms what the network needs to
   provide and have the network do the rest seems like the Holy Grail of
   intuition and ease-of-use for how to interact with a network.
   Accordingly, the term "intent" has caught on rapidly and spread like
   wildfire, being rapidly adopted also by SDN controllers and by
   management solutions, all proclaiming the higher-level abstractions
   exposed of their own northbound interfaces as "intent".  However,
   somewhat overlooked in all this is the fact that, as mentioned above,
   the concept of management or control abstraction hierarchies was not
   invented with Intent.  This concept has been known for a long time
   and variations of this concept incarnated in different forms in the
   past.  Specifically:

      Policy-based management has the goal of defining high-level
      policies that are subsequently translated and rendered into lower-
      level actions and parameter settings at devices.  Policies are
      frequently defined in terms of rules, consisting of events (that
      trigger a rule), conditions (that are assessed when the rule is
      triggered), and actions (that are carried out when the condition
      holds).  However, many different categories of policies and ways
      to define them exist, ranging from ACL-style matching rules to
      high-level declarative policy languages such as Ponder.

      Service models define end-to-end service instances, which are in
      turn mapped onto low-level configurations (often expressed via
      device data models) that are applied across devices and resources
      in the network.

   This raises the question in which ways intent, policy, and service
   models are really different.  Terms that are trending can become
   easily overloaded and may end up being used as synonyms for terms
   that had already been well-established before, not just for the new
   and differentiating concept for which they were introduced.  In order
   to avoid this situation, this document aims to provide a clear
   distinction between these terms and what they represent.
   Specifically, it aims to answer the question whether "intent" is just
   a new term for "policy" (or "service model") or whether it represents
   something genuinely different.

2.  Key Words

   The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
   "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.

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3.  Definitions and Acronyms

      ACL: Access Control List

      Intent: An abstract, high-level policy used to operate a network

      Policy: A rule, or 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.

4.  Introduction of Concepts

   The following subsections provide an overview of the concepts of
   service models, of policies respectively policy-based management, and
   of intent respectively intent-based management.  While the
   descriptions are intentionally kept brief and do not provide detailed
   tutorials, they should convey the bigger picture of the purpose of
   each concept and provide a sense where those concepts are similar and
   where they differ.  With this background, the differences between
   them are summarized in the subsequent section.

4.1.  Service Models

   A service model is a model that represents a service that is provided
   by a network to a user.  An example of a service could be a Layer 3
   VPN service, 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  Resources need to be allocated, such as IP addresses, 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).

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

   They involve a system, such as a controller, that provides
   provisioning logic.  Orchestration itself is conducted using a "push"
   model, in which the controller/manager initiates the operations as
   required, pushing down the specific configurations to the device.
   The device itself is typically agnostic to the service or the fact
   that its resources or configurations are part of a service/concept at
   a higher layer.

   Instantiated service models map to instantiated lower-layer 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 or provisioned top-to-bottom, and to keep
   track of the assignment of network resources.

4.2.  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 today present 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 an only provide a much-abridged and distilled

   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, rules follow consists of events
   (whose occurrence triggers a rule), conditions (that get assessed
   before any actions are actually "fired"), and actions that are
   carried out when the condition holds.

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   Policy-based management can be considered an imperative management
   paradigm: Policies specify precisely what needs to be done when.
   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
   simple control loops typically used to 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 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, which leads to operational scale and allows
   network operators and authors of policies to think in higher terms of
   abstraction than device specifics.

   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.  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, PBM architectures
   typically involve a central component from which policies are
   deployed across the network, and/or policy decisions served.

4.3.  Intent and Intent-Based Management

   In the context of Autonomic Networks, Intent is defined in as "an
   abstract, high-level policy used to operate a network".  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

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   distinction needs to be introduced that distinguishes intent clearly
   from other types of policies.

   Autonomic networks are expected to "self-manage" and operate with
   minimal outside intervention.  However, autonomic networks 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

   More specifically, intent is a declaration of high-level operational
   goals or services that are to be provided by the network, without
   specifying how to achieve them.  Those goals are defined in a manner
   that is purely declarative - they specify what to accomplish or what
   the desired outcome for the network operator is, not how to achieve
   it or even when to spring into action.  In addition, Intent (at least
   in an Autonomic Network) should be rendered by network devices
   themselves, i.e., translated into device specific rules and courses
   of action.  It should not be orchestrated or broken down by a higher-
   level, centralized system.  Intent holds for the network as a whole,
   not individual devices, and is hence 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.

   Intent thus constititutes declarative policy with a network-wide
   scope.  A human operator defines 'what' is expected, and the network
   computes a solution meeting the requirements.  This computation can
   occur in distributed or even decentralized fashion by auonomic
   functions that reside on network nodes.

   Other definitions of intent exist such as [ONF TR 523] 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.

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5.  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  Service model is a data model with dependencies onto lower models.
      It requires orchestration by a system; the logic to
      orchestrate/manage/provide the service model 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.

   o  Intent is a higher-level declarative policy that operates at the
      level of a network, not individual devices.  It is used to define
      operational goals and desired outcomes without the need to
      enumerate specific events, conditions, and actions.  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

6.  IANA Considerations

   Not applicable

7.  Security Considerations

   Not applicable

8.  References

8.1.  Normative References

   [RFC2119]  Bradner, S., "Key words for use in RFCs to Indicate
              Requirement Levels", BCP 14, RFC 2119,
              DOI 10.17487/RFC2119, March 1997,

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   [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,

   [RFC8174]  Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC
              2119 Key Words", BCP 14, RFC 8174, DOI 10.17487/RFC8174,
              May 2017, <>.

8.2.  Informative References

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

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

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

Authors' Addresses

   Alexander Clemm
   Futurewei Technologies, Inc.
   2330 Central Expressway
   Santa Clara,  CA 95050


   Laurent Ciavaglia
   Route de Villejust
   Nozay  91460


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


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