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Service Assurance for Intent-based Networking Architecture

The information below is for an old version of the document.
Document Type
This is an older version of an Internet-Draft that was ultimately published as RFC 9417.
Authors Benoît Claise , Jean Quilbeuf , Diego Lopez , Daniel Voyer , Thangam Arumugam
Last updated 2022-12-15 (Latest revision 2022-11-23)
Replaces draft-claise-opsawg-service-assurance-architecture
RFC stream Internet Engineering Task Force (IETF)
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Stream WG state Submitted to IESG for Publication
Document shepherd Michael Richardson
Shepherd write-up Show Last changed 2022-09-30
IESG IESG state Became RFC 9417 (Informational)
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Responsible AD Robert Wilton
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OPSAWG                                                         B. Claise
Internet-Draft                                               J. Quilbeuf
Intended status: Informational                                    Huawei
Expires: 27 May 2023                                            D. Lopez
                                                          Telefonica I+D
                                                                D. Voyer
                                                             Bell Canada
                                                             T. Arumugam
                                                     Cisco Systems, Inc.
                                                        23 November 2022

       Service Assurance for Intent-based Networking Architecture


   This document describes an architecture that aims at assuring that
   service instances are running as expected.  As services rely upon
   multiple sub-services provided by a variety of elements including the
   underlying network devices and functions, getting the assurance of a
   healthy service is only possible with a holistic view of all involved
   elements.  This architecture not only helps to correlate the service
   degradation with symptoms of a specific network component but also to
   list the services impacted by the failure or degradation of a
   specific network component.

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

   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 27 May 2023.

Copyright Notice

   Copyright (c) 2022 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 (
   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 Revised BSD License text as
   described in Section 4.e of the Trust Legal Provisions and are
   provided without warranty as described in the Revised BSD License.

Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
   2.  Terminology . . . . . . . . . . . . . . . . . . . . . . . . .   5
   3.  A Functional Architecture . . . . . . . . . . . . . . . . . .   7
     3.1.  Translating a Service Instance Configuration into an
           Assurance Graph . . . . . . . . . . . . . . . . . . . . .  10
       3.1.1.  Circular Dependencies . . . . . . . . . . . . . . . .  12
     3.2.  Intent and Assurance Graph  . . . . . . . . . . . . . . .  16
     3.3.  Subservices . . . . . . . . . . . . . . . . . . . . . . .  17
     3.4.  Building the Expression Graph from the Assurance Graph  .  17
     3.5.  Open Interfaces with YANG Modules . . . . . . . . . . . .  19
     3.6.  Handling Maintenance Windows  . . . . . . . . . . . . . .  19
     3.7.  Flexible Functional Architecture  . . . . . . . . . . . .  21
     3.8.  Time window for symptoms history  . . . . . . . . . . . .  22
     3.9.  New Assurance Graph Generation  . . . . . . . . . . . . .  22
   4.  Security Considerations . . . . . . . . . . . . . . . . . . .  23
   5.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .  24
   6.  Contributors  . . . . . . . . . . . . . . . . . . . . . . . .  24
   7.  References  . . . . . . . . . . . . . . . . . . . . . . . . .  24
     7.1.  Normative References  . . . . . . . . . . . . . . . . . .  24
     7.2.  Informative References  . . . . . . . . . . . . . . . . .  24
   Appendix A.  Changes between revisions  . . . . . . . . . . . . .  26
   Acknowledgements  . . . . . . . . . . . . . . . . . . . . . . . .  27
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  27

1.  Introduction

   Network service YANG modules [RFC8199] describe the configuration,
   state data, operations, and notifications of abstract representations
   of services implemented on one or multiple network elements.

   Service orchestrators use Network service YANG modules that will
   infer network-wide configuration and, therefore the invocation of the
   appropriate device modules (Section 3 of [RFC8969]).  Knowing that a
   configuration is applied doesn't imply that the provisioned service
   instance is up and running as expected.  For instance, the service
   might be degraded because of a failure in the network, the service
   quality may be degraded, or a service function may be reachable at

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   the IP level but does not provide its intended function.  Thus, the
   network operator must monitor the service's operational data at the
   same time as the configuration (Section 3.3 of [RFC8969]).  To feed
   that task, the industry has been standardizing on telemetry to push
   network element performance information (e.g.,

   A network administrator needs to monitor their network and services
   as a whole, independently of the management protocols.  With
   different protocols come different data models, and different ways to
   model the same type of information.  When network administrators deal
   with multiple management protocols, the network management entities
   have to perform the difficult and time-consuming job of mapping data
   models: e.g., the model used for configuration with the model used
   for monitoring when separate models or protocols are used.  This
   problem is compounded by a large, disparate set of data sources (MIB
   modules, YANG models [RFC7950], IPFIX information elements [RFC7011],
   syslog plain text [RFC5424], TACACS+ [RFC8907], RADIUS [RFC2865],
   etc.).  In order to avoid this data model mapping, the industry
   converged on model-driven telemetry to stream the service operational
   data, reusing the YANG models used for configuration.  Model-driven
   telemetry greatly facilitates the notion of closed-loop automation
   whereby events and updated operational state streamed from the
   network drive remediation changes back into the network.

   However, it proves difficult for network operators to correlate the
   service degradation with the network root cause.  For example, "Why
   does my layer 3 virtual private network (L3VPN) fail to connect?" or
   "Why is this specific service not highly responsive?".  The reverse,
   i.e., which services are impacted when a network component fails or
   degrades, is also important for operators.  For example, "Which
   services are impacted when this specific optic decibel milliwatt
   (dBm) begins to degrade?", "Which applications are impacted by an
   imbalance in this equal cost multiple paths (ECMP) bundle?", or "Is
   that issue actually impacting any other customers?".  This task
   usually falls under the so-called "Service Impact Analysis"
   functional block.

   In this document, we propose an architecture implementing Service
   Assurance for Intent-Based Networking (SAIN).  Intent-based
   approaches are often declarative, starting from a statement of "The
   service works as expected" and trying to enforce it.  However, some
   already defined services might have been designed using a different
   approach.  Aligned with Section 3.3 of [RFC7149], and instead of
   requiring a declarative intent as a starting point, this architecture
   focuses on already defined services and tries to infer the meaning of
   "The service works as expected".  To do so, the architecture works
   from an assurance graph, deduced from the configuration pushed to the

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   device for enabling the service instance.  If the SAIN orchestrator
   supports it, the service model (Section 2 of [RFC8309]) or the
   network model (Section 2.1 of [RFC8969]) can also be used to build
   the assurance graph.  In that case and if the service model includes
   the declarative intent as well, the SAIN orchestrator can rely on the
   declared intent instead of inferring it.  The assurance graph may
   also be explicitly completed to add an intent not exposed in the
   service model itself.

   The assurance graph of a service instance is decomposed into
   components, which are then assured independently.  The root of the
   assurance graph represents the service instance to assure, and its
   children represent components identified as its direct dependencies;
   each component can have dependencies as well.  Components involved in
   the assurance graph of a service are called subservices.  The SAIN
   orchestrator updates automatically the assurance graph when the
   service instance is modified.

   When a service is degraded, the SAIN architecture will highlight
   where in the assurance service graph to look, as opposed to going hop
   by hop to troubleshoot the issue.  More precisely, the SAIN
   architecture will associate to each service instance a list of
   symptoms originating from specific subservices, corresponding to
   components of the network.  These components are good candidates for
   explaining the source of a service degradation.  Not only can this
   architecture help to correlate service degradation with network root
   cause/symptoms, but it can deduce from the assurance graph the list
   of service instances impacted by a component degradation/failure.
   This added value informs the operational team where to focus its
   attention for maximum return.  Indeed, the operational team is likely
   to focus their priority on the degrading/failing components impacting
   the highest number of their customers, especially the ones with the
   SLA contracts involving penalties in case of failure.

   This architecture provides the building blocks to assure both
   physical and virtual entities and is flexible with respect to
   services and subservices, of (distributed) graphs, and of components
   (Section 3.7).

   The architecture presented in this document is completed by a set of
   YANG modules defined in a companion document
   [I-D.ietf-opsawg-service-assurance-yang].  These YANG modules
   properly define the interfaces between the various components of the
   architecture in order to foster interoperability.

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2.  Terminology

   SAIN agent: A functional component that communicates with a device, a
   set of devices, or another agent to build an expression graph from a
   received assurance graph and perform the corresponding computation of
   the health status and symptoms.  A SAIN agent might be running
   directly on the device it monitors.

   Assurance case: "An assurance case is a structured argument,
   supported by evidence, intended to justify that a system is
   acceptably assured relative to a concern (such as safety or security)
   in the intended operating environment" [Piovesan2017].

   Service instance: A specific instance of a service.

   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" [I-D.irtf-nmrg-ibn-concepts-definitions].

   Subservice: Part or functionality of the network system that can be
   independently assured as a single entity in assurance graph.

   Assurance graph: A Directed Acyclic Graph (DAG) representing the
   assurance case for one or several service instances.  The nodes (also
   known as vertices in the context of DAG) are the service instances
   themselves and the subservices, the edges indicate a dependency

   SAIN collector: A functional component that fetches or receives the
   computer-consumable output of the SAIN agent(s) and process it
   locally (including displaying it in a user-friendly form).

   DAG: Directed Acyclic Graph.

   ECMP: Equal Cost Multiple Paths

   Expression graph: A generic term for a DAG representing a computation
   in SAIN.  More specific terms are:

   *  Subservice expressions: Is an expression graph representing all
      the computations to execute for a subservice.

   *  Service expressions: Is an expression graph representing all the
      computations to execute for a service instance, i.e., including
      the computations for all dependent subservices.

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   *  Global computation graph: Is an expression graph representing all
      the computations to execute for all services instances (i.e., all
      computations performed).

   Dependency: The directed relationship between subservice instances in
   the assurance graph.

   Metric: A piece of information retrieved from the network running the
   assured service.

   Metric engine: A functional component, part of the SAIN agent, that
   maps metrics to a list of candidate metric implementations depending
   on the network element.

   Metric implementation: Actual way of retrieving a metric from a
   network element.

   Network service YANG module: describes the characteristics of a
   service as agreed upon with consumers of that service [RFC8199].

   Service orchestrator: Quoting RFC8199, "Network Service YANG Modules
   describe the characteristics of a service, as agreed upon with
   consumers of that service.  That is, a service module does not expose
   the detailed configuration parameters of all participating network
   elements and features but describes an abstract model that allows
   instances of the service to be decomposed into instance data
   according to the Network Element YANG Modules of the participating
   network elements.  The service-to-element decomposition is a separate
   process; the details depend on how the network operator chooses to
   realize the service.  For the purpose of this document, the term
   "orchestrator" is used to describe a system implementing such a

   SAIN orchestrator: A functional component that is in charge of
   fetching the configuration specific to each service instance and
   converting it into an assurance graph.

   Health status: Score and symptoms indicating whether a service
   instance or a subservice is "healthy".  A non-maximal score must
   always be explained by one or more symptoms.

   Health score: Integer ranging from 0 to 100 indicating the health of
   a subservice.  A score of 0 means that the subservice is broken, a
   score of 100 means that the subservice in question is operating as

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   Strongly connected component: subset of a directed graph such that
   there is a (directed) path from any node of the subset to any other
   node.  A DAG does not contain any strongly connected component.

   Symptom: Reason explaining why a service instance or a subservice is
   not completely healthy.

3.  A Functional Architecture

   The goal of SAIN is to assure that service instances are operating as
   expected (i.e., the observed service is matching the expected
   service) and if not, to pinpoint what is wrong.  More precisely, SAIN
   computes a score for each service instance and outputs symptoms
   explaining that score.  The only valid situation where no symptoms
   are returned is when the score is maximal, indicating that no issues
   were detected for that service instance.  The score augmented with
   the symptoms is called the health status.

   The SAIN architecture is a generic architecture, applicable to
   multiple environments (e.g. wireline, wireless), but also different
   domains (e.g. 5G network function virtualization (NFV) domain with a
   virtual infrastructure manager (VIM), etc.), and as already noted,
   for physical or virtual devices, as well as virtual functions.
   Thanks to the distributed graph design principle, graphs from
   different environments/orchestrator can be combined to obtain the
   graph of a service instance that spans over multiple domains.

   As an example of a service, let us consider a point-to-point level 2
   virtual private network (L2VPN).  [RFC8466] specifies the parameters
   for such a service.  Examples of symptoms might be symptoms reported
   by specific subservices "Interface has high error rate" or "Interface
   flapping", or "Device almost out of memory" as well as symptoms more
   specific to the service such as "Site disconnected from VPN".

   To compute the health status of an instance of such a service, the
   service definition is decomposed into an assurance graph formed by
   subservices linked through dependencies.  Each subservice is then
   turned into an expression graph that details how to fetch metrics
   from the devices and compute the health status of the subservice.
   The subservice expressions are combined according to the dependencies
   between the subservices in order to obtain the expression graph which
   computes the health status of the service instance.

   The overall SAIN architecture is presented in Figure 1.  Based on the
   service configuration provided by the service orchestrator, the SAIN
   orchestrator decomposes the assurance graph.  It then sends to the
   SAIN agents the assurance graph along with some other configuration
   options.  The SAIN agents are responsible for building the expression

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   graph and computing the health statuses in a distributed manner.  The
   collector is in charge of collecting and displaying the current
   inferred health status of the service instances and subservices.  The
   collector also detects changes in the assurance graph structures, for
   instance when a switchover from primary to backup path occurs, and
   forwards to the orchestrator, which reconfigures the agents.
   Finally, the automation loop is closed by having the SAIN collector
   providing feedback to the network/service orchestrator.

   In order to make agents, orchestrators and collectors from different
   vendors interoperable, their interface is defined as a YANG model in
   a companion document [I-D.ietf-opsawg-service-assurance-yang].  In
   Figure 1, the communications that are normalized by this YANG model
   are tagged with a "Y".  The use of this YANG model is further
   explained in Section 3.5.

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        | Service         |
        | Orchestrator    |<----------------------+
        |                 |                       |
        +-----------------+                       |
           |            ^                         |
           |            | Network                 |
           |            | Service                 | Feedback
           |            | Instance                | Loop
           |            | Configuration           |
           |            |                         |
           |            V                         |
           |        +-----------------+  Graph  +-------------------+
           |        | SAIN            | updates | SAIN              |
           |        | Orchestrator    |<--------| Collector         |
           |        +-----------------+         +-------------------+
           |            |                          ^
           |           Y| Configuration            | Health Status
           |            | (assurance graph)       Y| (Score + Symptoms)
           |            V                          | Streamed
           |     +-------------------+             | via Telemetry
           |     |+-------------------+            |
           |     ||+-------------------+           |
           |     +|| SAIN              |-----------+
           |      +| agent             |
           |       +-------------------+
           |               ^ ^ ^
           |               | | |
           |               | | |  Metric Collection
           V               V V V
       |           Network System                                    |
       |                                                             |

                        Figure 1: SAIN Architecture

   In order to produce the score assigned to a service instance, the
   various involved components perform the following tasks:

   *  Analyze the configuration pushed to the network device(s) for
      configuring the service instance.  From there, determine which
      information (called a metric) must be collected from the device(s)
      and which operations to apply to the metrics to compute the health

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   *  Stream (via telemetry [RFC8641]) operational and config metric
      values when possible, else continuously poll.

   *  Continuously compute the health status of the service instances,
      based on the metric values.

   The SAIN architecture requires time synchronization, with Network
   Time Protocol (NTP) [RFC5905] as a candidate, between all elements:
   monitored entities, SAIN agents, Service orchestrator, the SAIN
   collector, as well as the SAIN orchestrator.  This guarantees the
   correlations of all symptoms in the system, correlated with the right
   assurance graph version.

3.1.  Translating a Service Instance Configuration into an Assurance

   In order to structure the assurance of a service instance, the SAIN
   orchestrator decomposes the service instance into so-called
   subservice instances.  Each subservice instance focuses on a specific
   feature or subpart of the service.

   The decomposition into subservices is an important function of the
   architecture, for the following reasons:

   *  The result of this decomposition provides a relational picture of
      a service instance, that can be represented as a graph (called
      assurance graph) to the operator.

   *  Subservices provide a scope for particular expertise and thereby
      enable contribution from external experts.  For instance, the
      subservice dealing with the optics health should be reviewed and
      extended by an expert in optical interfaces.

   *  Subservices that are common to several service instances are
      reused for reducing the amount of computation needed.  For
      instance, the subservice assuring a given interface is reused by
      any service instance relying on that interface.

   The assurance graph of a service instance is a DAG representing the
   structure of the assurance case for the service instance.  The nodes
   of this graph are service instances or subservice instances.  Each
   edge of this graph indicates a dependency between the two nodes at
   its extremities: the service or subservice at the source of the edge
   depends on the service or subservice at the destination of the edge.

   Figure 2 depicts a simplistic example of the assurance graph for a
   tunnel service.  The node at the top is the service instance, the
   nodes below are its dependencies.  In the example, the tunnel service

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   instance depends on the "peer1" and "peer2" tunnel interfaces, which
   in turn depend on the respective physical interfaces, which finally
   depend on the respective "peer1" and "peer2" devices.  The tunnel
   service instance also depends on the IP connectivity that depends on
   the IS-IS routing protocol.

                            | Tunnel           |
                            | Service Instance |
                 |                    |                   |
                 v                    v                   v
            +-------------+    +--------------+    +-------------+
            | Peer1       |    | IP           |    | Peer2       |
            | Tunnel      |    | Connectivity |    | Tunnel      |
            | Interface   |    |              |    | Interface   |
            +-------------+    +--------------+    +-------------+
                   |                  |                  |
                   |    +-------------+--------------+   |
                   |    |             |              |   |
                   v    v             v              v   v
            +-------------+    +-------------+     +-------------+
            | Peer1       |    | IS-IS       |     | Peer2       |
            | Physical    |    | Routing     |     | Physical    |
            | Interface   |    | Protocol    |     | Interface   |
            +-------------+    +-------------+     +-------------+
                   |                                     |
                   v                                     v
            +-------------+                        +-------------+
            |             |                        |             |
            | Peer1       |                        | Peer2       |
            | Device      |                        | Device      |
            +-------------+                        +-------------+

                     Figure 2: Assurance Graph Example

   Depicting the assurance graph helps the operator to understand (and
   assert) the decomposition.  The assurance graph shall be maintained
   during normal operation with addition, modification and removal of
   service instances.  A change in the network configuration or topology
   shall automatically be reflected in the assurance graph.  As a first
   example, a change of routing protocol from IS-IS to OSPF would change
   the assurance graph accordingly.  As a second example, assuming that
   ECMP is in place for the source router for that specific tunnel; in
   that case, multiple interfaces must now be monitored, on top of the
   monitoring the ECMP health itself.

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3.1.1.  Circular Dependencies

   The edges of the assurance graph represent dependencies.  An
   assurance graph is a DAG if and only if there are no circular
   dependencies among the subservices, and every assurance graph should
   avoid circular dependencies.  However, in some cases, circular
   dependencies might appear in the assurance graph.

   First, the assurance graph of a whole system is obtained by combining
   the assurance graph of every service running on that system.  Here
   combining means that two subservices having the same type and the
   same parameters are in fact the same subservice and thus a single
   node in the graph.  For instance, the subservice of type "device"
   with the only parameter (the device id) set to "PE1" will appear only
   once in the whole assurance graph even if several service instances
   rely on that device.  Now, if two engineers design assurance graphs
   for two different services, and engineer A decides that an interface
   depends on the link it is connected to, but engineer B decides that
   the link depends on the interface it is connected to, then when
   combining the two assurance graphs, we will have a circular
   dependency interface -> link -> interface.

   Another case possibly resulting in circular dependencies is when
   subservices are not properly identified.  Assume that we want to
   assure a cloud-based computing cluster that runs containers.  We
   could represent the cluster by a subservice and the network service
   connecting containers on the cluster by another subservice.  We will
   likely model that the network service depends on the cluster, because
   the network service runs in a container supported by the cluster.
   Conversely, the cluster depends on the network service for
   connectivity between containers, which creates a circular dependency.
   A finer decomposition might distinguish between the resources for
   executing containers (a part of our cluster subservice) and the
   communication between the containers (which could be modelled in the
   same way as communication between routers).

   In any case, it is likely that circular dependencies will show up in
   the assurance graph.  A first step would be to detect circular
   dependencies as soon as possible in the SAIN architecture.  Such a
   detection could be carried out by the SAIN orchestrator.  Whenever a
   circular dependency is detected, the newly added service would not be
   monitored until more careful modelling or alignment between the
   different teams (engineer A and B) remove the circular dependency.

   As more elaborate solution we could consider a graph transformation:

   *  Decompose the graph into strongly connected components.

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   *  For each strongly connected component:

      -  Remove all edges between nodes of the strongly connected

      -  Add a new "top" node for the strongly connected component

      -  For each edge pointing to a node in the strongly connected
         component, change the destination to the "top" node

      -  Add a dependency from the top node to every node in the
         strongly connected component.

   Such an algorithm would include all symptoms detected by any
   subservice in one of the strongly component and make it available to
   any subservice that depends on it.  Figure 3 shows an example of such
   a transformation.  On the left-hand side, the nodes c, d, e and f
   form a strongly connected component.  The status of node a should
   depend on the status of nodes c, d, e, f, g, and h, but this is hard
   to compute because of the circular dependency.  On the right hand-
   side, a depends on all these nodes as well, but there the circular
   dependency has been removed.

         +---+    +---+          |                +---+    +---+
         | a |    | b |          |                | a |    | b |
         +---+    +---+          |                +---+    +---+
           |        |            |                  |        |
           v        v            |                  v        v
         +---+    +---+          |                +------------+
         | c |--->| d |          |                |    top     |
         +---+    +---+          |                +------------+
           ^        |            |               /   |      |   \
           |        |            |              /    |      |    \
           |        v            |             v     v      v     v
         +---+    +---+          |          +---+  +---+  +---+  +---+
         | f |<---| e |          |          | f |  | c |  | d |  | e |
         +---+    +---+          |          +---+  +---+  +---+  +---+
           |        |            |            |                    |
           v        v            |            v                    v
         +---+    +---+          |          +---+                +---+
         | g |    | h |          |          | g |                | h |
         +---+    +---+          |          +---+                +---+

            Before                                     After
         Transformation                           Transformation

                       Figure 3: Graph transformation

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   We consider a concrete example to illustrate this transformation.
   Let's assume that Engineer A is building an assurance graph dealing
   with IS-IS and Engineer B is building an assurance graph dealing with
   OSPF.  The graph from Engineer A could contain the following:

                   | IS-IS Link |
                   | Phys. Link |
                     |       |
                     v       v
          +-------------+  +-------------+
          | Interface 1 |  | Interface 2 |
          +-------------+  +-------------+

           Figure 4: Fragment of assurance graph from Engineer A

   The graph from Engineer B could contain the following:

                   | OSPF Link  |
                     |   |   |
                     v   |   v
        +-------------+  |  +-------------+
        | Interface 1 |  |  | Interface 2 |
        +-------------+  |  +-------------+
                      |  |   |
                      v  v   v
                   | Phys. Link |

           Figure 5: Fragment of assurance graph from Engineer B

   Each Interface subservice and the Physical Link subservice are common
   to both fragments above.  Each of these subservice appears only once
   in the graph merging the two fragments.  Dependencies from both
   fragments are included in the merged graph, resulting in a circular

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         +------------+      +------------+
         | IS-IS Link |      | OSPF Link  |---+
         +------------+      +------------+   |
               |               |     |        |
               |     +-------- +     |        |
               v     v               |        |
         +------------+              |        |
         | Phys. Link |<-------+     |        |
         +------------+        |     |        |
           |  ^     |          |     |        |
           |  |     +-------+  |     |        |
           v  |             v  |     v        |
         +-------------+  +-------------+     |
         | Interface 1 |  | Interface 2 |     |
         +-------------+  +-------------+     |
               ^                              |
               |                              |

                   Figure 6: Merging graphs from A and B

   The solution presented above would result in graph looking as
   follows, where a new "empty" node is included.  Using that
   transformation, all dependencies are indirectly satisfied for the
   nodes outside the circular dependency, in the sense that both IS-IS
   and OSPF links have indirect dependencies to the two interfaces and
   the link.  However, the dependencies between the link and the
   interfaces are lost as they were causing the circular dependency.

               +------------+      +------------+
               | IS-IS Link |      | OSPF Link  |
               +------------+      +------------+
                          |          |
                          v          v
                         |  empty     |
                   |           |             |
                   v           v             v
         +-------------+ +------------+ +-------------+
         | Interface 1 | | Phys. Link | | Interface 2 |
         +-------------+ +------------+ +-------------+

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       Figure 7: Removing circular dependencies after merging graphs
                                from A and B

3.2.  Intent and Assurance Graph

   The SAIN orchestrator analyzes the configuration of a service
   instance to:

   *  Try to capture the intent of the service instance, i.e., what is
      the service instance trying to achieve.  At least, this requires
      the SAIN orchestrator to know the YANG modules that are being
      configured on the devices to enable the service.  Note that if the
      service model or the network model is known to the SAIN
      orchestrator, the latter can exploit it.  In that case, the intent
      could be directly extracted and include more details, such as the
      notion of sites for a VPN, which is out of scope of the device

   *  Decompose the service instance into subservices representing the
      network features on which the service instance relies.

   The SAIN orchestrator must be able to analyze configuration pushed to
   various devices for configuring a service instance and produce the
   assurance graph for that service instance.

   To schematize what a SAIN orchestrator does, assume that the
   configuration for a service instance touches two devices and
   configure on each device a virtual tunnel interface.  Then:

   *  Capturing the intent would start by detecting that the service
      instance is actually a tunnel between the two devices, and stating
      that this tunnel must be functional.  This solution is minimally
      invasive as it does not require modifying nor knowing the service
      model.  If the service model or network model is known by the SAIN
      orchestrator, it can be used to further capture the intent and
      include more information such as Service Level Objectives (SLO).
      For instance, the latency and bandwidth requirements for the
      tunnel, if present in the service model

   *  Decomposing the service instance into subservices would result in
      the assurance graph depicted in Figure 2, for instance.

   The assurance graph, or more precisely the subservices and
   dependencies that a SAIN orchestrator can instantiate, should be
   curated.  The organization of such a process is out-of-scope for this
   document and should aim to:

   *  Ensure that existing subservices are reused as much as possible.

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   *  Avoid circular dependencies.

   To be applied, SAIN requires a mechanism mapping a service instance
   to the configuration actually required on the devices for that
   service instance to run.  While the Figure 1 makes a distinction
   between the SAIN orchestrator and a different component providing the
   service instance configuration, in practice those two components are
   mostly likely combined.  The internals of the orchestrator are out of
   scope of this document.

3.3.  Subservices

   A subservice corresponds to subpart or a feature of the network
   system that is needed for a service instance to function properly.
   In the context of SAIN, a subservice also defines its assurance, that
   is the method for assuring that a subservice behaves correctly.

   Subservices, just as with services, have high-level parameters that
   specify the instance to be assured.  The needed parameters depend on
   the subservice type.  For example, assuring a device requires a
   specific deviceId as parameter.  For example, assuring an interface
   requires a specific combination of deviceId and interfaceId.

   When designing a new type of subservice, one should carefully define
   what is the assured object or functionality.  Then, the parameters
   must be chosen as a minimal set that completely identify the object
   (see examples from the previous paragraph).  Parameters cannot change
   during the lifecycle of a subservice.  For instance, an IP address is
   a good parameter when assuring a connectivity towards that address
   (i.e. a given device can reach a given IP address), however it's a
   not a good parameter to identify an interface as the IP address
   assigned to that interface can be changed.

   A subservice is also characterized by a list of metrics to fetch and
   a list of operations to apply to these metrics in order to infer a
   health status.

3.4.  Building the Expression Graph from the Assurance Graph

   From the assurance graph is derived a so-called global computation
   graph.  First, each subservice instance is transformed into a set of
   subservice expressions that take metrics and constants as input
   (i.e., sources of the DAG) and produce the status of the subservice,
   based on some heuristics.  For instance, the health of an interface
   is 0 (minimal score) with the symptom "interface admin-down" if the
   interface is disabled in the configuration.  Then for each service
   instance, the service expressions are constructed by combining the
   subservice expressions of its dependencies.  The way service

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   expressions are combined depends on the dependency types (impacting
   or informational).  Finally, the global computation graph is built by
   combining the service expressions.  In other words, the global
   computation graph encodes all the operations needed to produce health
   statuses from the collected metrics.

   The two types of dependencies for combining subservices are:

      Informational Dependency: Type of dependency whose health score
      does not impact the health score of its parent subservice or
      service instance(s) in the assurance graph.  However, the symptoms
      should be taken into account in the parent service instance or
      subservice instance(s), for informational reasons.

      Impacting Dependency: Type of dependency whose score impacts the
      score of its parent subservice or service instance(s) in the
      assurance graph.  The symptoms are taken into account in the
      parent service instance or subservice instance(s), as the
      impacting reasons.

   The set of dependency type presented here is not exhaustive.  More
   specific dependency types can be defined by extending the YANG model.
   For instance, a connectivity subservice depending on several path
   subservices is only partially impacted if only one of these paths
   fails.  Adding these new dependency types requires defining the
   corresponding operation for combining statuses of subservices.

   Subservices shall not be dependent on the protocol used to retrieve
   the metrics.  To justify this, let's consider the interface
   operational status.  Depending on the device capabilities, this
   status can be collected by an industry-accepted YANG module (IETF,
   Openconfig [OpenConfig]), by a vendor-specific YANG module, or even
   by a MIB module.  If the subservice was dependent on the mechanism to
   collect the operational status, then we would need multiple
   subservice definitions in order to support all different mechanisms.
   This also implies that, while waiting for all the metrics to be
   available via standard YANG modules, SAIN agents might have to
   retrieve metric values via non-standard YANG models, via MIB modules,
   Command Line Interface (CLI), etc., effectively implementing a
   normalization layer between data models and information models.

   In order to keep subservices independent of metric collection method,
   or, expressed differently, to support multiple combinations of
   platforms, OSes, and even vendors, the architecture introduces the
   concept of "metric engine".  The metric engine maps each device-
   independent metric used in the subservices to a list of device-
   specific metric implementations that precisely define how to fetch
   values for that metric.  The mapping is parameterized by the

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   characteristics (model, OS version, etc.) of the device from which
   the metrics are fetched.  This metric engine is included in the SAIN

3.5.  Open Interfaces with YANG Modules

   The interfaces between the architecture components are open thanks to
   the YANG modules specified in
   [I-D.ietf-opsawg-service-assurance-yang]; they specify objects for
   assuring network services based on their decomposition into so-called
   subservices, according to the SAIN architecture.

   These modules are intended for the following use cases:

   *  Assurance graph configuration:

      -  Subservices: configure a set of subservices to assure, by
         specifying their types and parameters.

      -  Dependencies: configure the dependencies between the
         subservices, along with their types.

   *  Assurance telemetry: export the health status of the subservices,
      along with the observed symptoms.

   Some examples of YANG instances can be found in Appendix A of

3.6.  Handling Maintenance Windows

   Whenever network components are under maintenance, the operator wants
   to inhibit the emission of symptoms from those components.  A typical
   use case is device maintenance, during which the device is not
   supposed to be operational.  As such, symptoms related to the device
   health should be ignored.  Symptoms related to the device-specific
   subservices, such as the interfaces, might also be ignored because
   their state changes is probably the consequence of the maintenance.

   The ietf-service-assurance model proposed in
   [I-D.ietf-opsawg-service-assurance-yang] enables flagging subservices
   as under maintenance, and, in that case, requires a string that
   identifies the person or process who requested the maintenance.  When
   a service or subservice is flagged as under maintenance, it must
   report a generic "Under Maintenance" symptom, for propagation towards
   subservices that depend on this specific subservice.  Any other
   symptom from this service, or by one of its impacting dependencies
   must not be reported.

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   We illustrate this mechanism on three independent examples based on
   the assurance graph depicted in Figure 2:

   *  Device maintenance, for instance upgrading the device OS.  The
      operator flags the subservice "Peer1" device as under maintenance.
      This inhibits the emission of symptoms, except "Under
      Maintenance", from "Peer1 Physical Interface", "Peer1 Tunnel
      Interface" and "Tunnel Service Instance".  All other subservices
      are unaffected.

   *  Interface maintenance, for instance replacing a broken optic.  The
      operator flags the subservice "Peer1 Physical Interface" as under
      maintenance.  This inhibits the emission of symptoms, except
      "Under Maintenance" from "Peer 1 Tunnel Interface" and "Tunnel
      Service Instance".  All other subservices are unaffected.

   *  Routing protocol maintenance, for instance modifying parameters or
      redistribution.  The operator marks the subservice "IS-IS Routing
      Protocol" as under maintenance.  This inhibits the emission of
      symptoms, except "Under Maintenance", from "IP connectivity" and
      "Tunnel Service Instance".  All other subservices are unaffected.

   In each example above, the subservice under maintenance is completely
   impacting the service instance, putting it under maintenance as well.
   There are use cases where the subservice under maintenance only
   partially impacts the service instance.  For instance, consider a
   service instance supported by both a primary and backup path.  If a
   subservice impacting the primary path is under maintenance, the
   service instance might still be functional but degraded.  In that
   case, the status of the service instance might include "Primary path
   Under Maintenance", "No redundancy" as well as other symptoms from
   the backup path to explain the lower health score.  In general, the
   computation of the service instance status from the subservices is
   done in the SAIN collector whose implementation is out of scope for
   this document.

   The maintenance of a subservice might modify or hide modifications of
   the structure of the assurance graph.  Therefore, unflagging a
   subservice as under maintenance should trigger an update of the
   assurance graph.

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3.7.  Flexible Functional Architecture

   The SAIN architecture is flexible in terms of components.  While the
   SAIN architecture in Figure 1 makes a distinction between two
   components, the service orchestrator and the SAIN orchestrator, in
   practice those two components are mostly likely combined.  Similarly,
   the SAIN agents are displayed in Figure 1 as being separate
   components.  Practically, the SAIN agents could be either independent
   components or directly integrated in monitored entities.  A practical
   example is an agent in a router.

   The SAIN architecture is also flexible in terms of services and
   subservices.  In the proposed architecture, the SAIN orchestrator is
   coupled to a service orchestrator which defines the kinds of services
   that the architecture handles.  Most examples in this document deal
   with the notion of Network Service YANG modules, with well-known
   services such as L2VPN or tunnels.  However, the concept of services
   is general enough to cross into different domains.  One of them is
   the domain of service management on network elements, which also
   require their own assurance.  Examples include a DHCP server on a
   Linux server, a data plane, an IPFIX export, etc.  The notion of
   "service" is generic in this architecture and depends on the service
   orchestrator and underlying network system, as illustrated by the
   following examples:

   *  if a main service orchestrator coordinates several lower level
      controllers, a service for the controller can be a subservice from
      the point of view of the orchestrator.

   *  A DHCP server/data plane/IPFIX export can be considered as
      subservices for a device.

   *  A routing instance can be considered as a subservice for a L3VPN.

   *  A tunnel can considered as a subservice for an application in the

   *  A service function can be considered as a subservice for a service
      function chain [RFC7665].

   The assurance graph is created to be flexible and open, regardless of
   the subservice types, locations, or domains.

   The SAIN architecture is also flexible in terms of distributed
   graphs.  As shown in Figure 1, the architecture comprises several
   agents.  Each agent is responsible for handling a subgraph of the
   assurance graph.  The collector is responsible for fetching the sub-
   graphs from the different agents and gluing them together.  As an

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   example, in the graph from Figure 2, the subservices relative to Peer
   1 might be handled by a different agent than the subservices relative
   to Peer 2 and the Connectivity and IS-IS subservices might be handled
   by yet another agent.  The agents will export their partial graph and
   the collector will stitch them together as dependencies of the
   service instance.

   And finally, the SAIN architecture is flexible in terms of what it
   monitors.  Most, if not all examples, in this document refer to
   physical components but this is not a constraint.  Indeed, the
   assurance of virtual components would follow the same principles and
   an assurance graph composed of virtualized components (or a mix of
   virtualized and physical ones) is supported by this architecture.

3.8.  Time window for symptoms history

   The health status reported via the YANG modules contains, for each
   subservice, the list of symptoms.  Symptoms have a start and end
   date, making it is possible to report symptoms that are no longer

   The SAIN agent might have to remove some symptoms for specific
   subservice symptoms, because there are outdated and not relevant any
   longer, or simply because the SAIN agent needs to free up some space.
   Regardless of the reason, it's important for a SAIN collector
   (re-)connecting to a SAIN agent to understand the effect of this
   garbage collection.

   Therefore, the SAIN agent contains a YANG object specifying the date
   and time at which the symptoms' history starts for the subservice
   instances.  The subservice reports only symptoms that are occurring
   or that have been occurring after the history start date.

3.9.  New Assurance Graph Generation

   The assurance graph will change over time, because services and
   subservices come and go (changing the dependencies between
   subservices), or simply because a subservice is now under
   maintenance.  Therefore, an assurance graph version must be
   maintained, along with the date and time of its last generation.  The
   date and time of a particular subservice instance (again dependencies
   or under maintenance) might be kept.  From a client point of view, an
   assurance graph change is triggered by the value of the assurance-
   graph-version and assurance-graph-last-change YANG leaves.  At that
   point in time, the client (collector) follows the following process:

   *  Keep the previous assurance-graph-last-change value (let's call it
      time T)

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   *  Run through all subservice instances and process the subservice
      instances for which the last-change is newer that the time T

   *  Keep the new assurance-graph-last-change as the new referenced
      date and time

4.  Security Considerations

   The SAIN architecture helps operators to reduce the mean time to
   detect and mean time to repair.  However, the SAIN agents must be
   secured: a compromised SAIN agent may be sending wrong root causes or
   symptoms to the management systems.  This can be partially achieved
   by correctly setting permissions of each node in the YANG model as
   described in the companion document

   Except for the configuration of telemetry, the agents do not need
   "write access" to the devices they monitor.  This configuration is
   applied with a YANG module, whose protection is covered by Secure
   Shell (SSH) [RFC6242] for NETCONF or TLS [RFC8446] for RESTCONF.
   Devices should be configured so that agents have their own
   credentials with write access only for the YANG nodes configuring the

   The data collected by SAIN could potentially be compromising to the
   network or provide more insight into how the network is designed.
   Considering the data that SAIN requires (including CLI access in some
   cases), one should weigh data access concerns with the impact that
   reduced visibility will have on being able to rapidly identify root

   For building the assurance graph, the SAIN orchestrator needs to
   obtain the configuration from the the service orchestrator.  The
   latter should restrict access of the SAIN orchestrator to information
   needed to build the assurance graph.

   If a closed loop system relies on this architecture then the well
   known issue of those systems also applies, i.e., a lying device or
   compromised agent could trigger partial reconfiguration of the
   service or network.  The SAIN architecture neither augments nor
   reduces this risk.  An extension of SAIN, out of scope for this
   document, could detect discrepancies between symptoms reported by
   different agents and thus detect anomalies if an agent or a device is

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   If NTP service goes down, the devices clocks might lose their
   synchronization.  In that case, correlating information from
   different devices, such as detecting symptoms about a link or
   correlating symptoms from different devices, will give inaccurate

5.  IANA Considerations

   This document includes no request to IANA.

6.  Contributors

   *  Youssef El Fathi

   *  Eric Vyncke

7.  References

7.1.  Normative References

              Claise, B., Quilbeuf, J., Lucente, P., Fasano, P., and T.
              Arumugam, "YANG Modules for Service Assurance", Work in
              Progress, Internet-Draft, draft-ietf-opsawg-service-
              assurance-yang-09, 7 November 2022,

   [RFC8309]  Wu, Q., Liu, W., and A. Farrel, "Service Models
              Explained", RFC 8309, DOI 10.17487/RFC8309, January 2018,

   [RFC8969]  Wu, Q., Ed., Boucadair, M., Ed., Lopez, D., Xie, C., and
              L. Geng, "A Framework for Automating Service and Network
              Management with YANG", RFC 8969, DOI 10.17487/RFC8969,
              January 2021, <>.

7.2.  Informative References

              Wu, B., Wu, Q., Boucadair, M., de Dios, O. G., and B. Wen,
              "A YANG Model for Network and VPN Service Performance
              Monitoring", Work in Progress, Internet-Draft, draft-ietf-
              opsawg-yang-vpn-service-pm-15, 11 November 2022,

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              Clemm, A., Ciavaglia, L., Granville, L. Z., and J.
              Tantsura, "Intent-Based Networking - Concepts and
              Definitions", Work in Progress, Internet-Draft, draft-
              irtf-nmrg-ibn-concepts-definitions-09, 24 March 2022,

              "OpenConfig", <>.

              Piovesan, A. and E. Griffor, "Reasoning About Safety and
              Security: The Logic of Assurance", 2017.

   [RFC2865]  Rigney, C., Willens, S., Rubens, A., and W. Simpson,
              "Remote Authentication Dial In User Service (RADIUS)",
              RFC 2865, DOI 10.17487/RFC2865, June 2000,

   [RFC5424]  Gerhards, R., "The Syslog Protocol", RFC 5424,
              DOI 10.17487/RFC5424, March 2009,

   [RFC5905]  Mills, D., Martin, J., Ed., Burbank, J., and W. Kasch,
              "Network Time Protocol Version 4: Protocol and Algorithms
              Specification", RFC 5905, DOI 10.17487/RFC5905, June 2010,

   [RFC6242]  Wasserman, M., "Using the NETCONF Protocol over Secure
              Shell (SSH)", RFC 6242, DOI 10.17487/RFC6242, June 2011,

   [RFC7011]  Claise, B., Ed., Trammell, B., Ed., and P. Aitken,
              "Specification of the IP Flow Information Export (IPFIX)
              Protocol for the Exchange of Flow Information", STD 77,
              RFC 7011, DOI 10.17487/RFC7011, September 2013,

   [RFC7149]  Boucadair, M. and C. Jacquenet, "Software-Defined
              Networking: A Perspective from within a Service Provider
              Environment", RFC 7149, DOI 10.17487/RFC7149, March 2014,

   [RFC7665]  Halpern, J., Ed. and C. Pignataro, Ed., "Service Function
              Chaining (SFC) Architecture", RFC 7665,
              DOI 10.17487/RFC7665, October 2015,

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   [RFC7950]  Bjorklund, M., Ed., "The YANG 1.1 Data Modeling Language",
              RFC 7950, DOI 10.17487/RFC7950, August 2016,

   [RFC8199]  Bogdanovic, D., Claise, B., and C. Moberg, "YANG Module
              Classification", RFC 8199, DOI 10.17487/RFC8199, July
              2017, <>.

   [RFC8446]  Rescorla, E., "The Transport Layer Security (TLS) Protocol
              Version 1.3", RFC 8446, DOI 10.17487/RFC8446, August 2018,

   [RFC8466]  Wen, B., Fioccola, G., Ed., Xie, C., and L. Jalil, "A YANG
              Data Model for Layer 2 Virtual Private Network (L2VPN)
              Service Delivery", RFC 8466, DOI 10.17487/RFC8466, October
              2018, <>.

   [RFC8641]  Clemm, A. and E. Voit, "Subscription to YANG Notifications
              for Datastore Updates", RFC 8641, DOI 10.17487/RFC8641,
              September 2019, <>.

   [RFC8907]  Dahm, T., Ota, A., Medway Gash, D.C., Carrel, D., and L.
              Grant, "The Terminal Access Controller Access-Control
              System Plus (TACACS+) Protocol", RFC 8907,
              DOI 10.17487/RFC8907, September 2020,

Appendix A.  Changes between revisions

   [[RFC editor: please remove this section before publication.]]

   v11 - 12

   *  Addressing comments from Last call

   v10 - v11

   *  Adding reference to example of network performance model

   v09 - v10

   *  Addressing comments from Rob Wilton

   v08 - v09

   *  Addressing comments from Michael Richardson

   v07 - v08

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   *  Propagating removal of under-maintenance flag from the YANG module


      Addressing comments from Dhruv Dhody and applying pending changes

   v03 - v04

   *  Address comments from Mohamed Boucadair

   v00 - v01

   *  Cover the feedback received during the WG call for adoption


   The authors would like to thank Stephane Litkowski, Charles Eckel,
   Rob Wilton, Vladimir Vassiliev, Gustavo Alburquerque, Stefan Vallin,
   Eric Vyncke, Mohamed Boucadair, Dhruv Dhody, Michael Richardson and
   Rob Wilton for their reviews and feedback.

Authors' Addresses

   Benoit Claise

   Jean Quilbeuf

   Diego R. Lopez
   Telefonica I+D
   Don Ramon de la Cruz, 82
   Madrid  28006

   Dan Voyer
   Bell Canada

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   Thangam Arumugam
   Cisco Systems, Inc.
   Milpitas (California),
   United States of America

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