RFC9417: Service Assurance for Intent-Based Networking Architecture

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Internet Engineering Task Force (IETF)                         B. Claise
Request for Comments: 9417                                   J. Quilbeuf
Category: Informational                                           Huawei
ISSN: 2070-1721                                                 D. Lopez
                                                          Telefonica I+D
                                                                D. Voyer
                                                             Bell Canada
                                                             T. Arumugam
                                                               July 2023

       Service Assurance for Intent-Based Networking Architecture


   This document describes an architecture that provides some assurance
   that service instances are running as expected.  As services rely
   upon multiple subservices 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, it also lists the services impacted by the failure or
   degradation of a specific network component.

Status of This Memo

   This document is not an Internet Standards Track specification; it is
   published for informational purposes.

   This document is a product of the Internet Engineering Task Force
   (IETF).  It represents the consensus of the IETF community.  It has
   received public review and has been approved for publication by the
   Internet Engineering Steering Group (IESG).  Not all documents
   approved by the IESG are candidates for any level of Internet
   Standard; see Section 2 of RFC 7841.

   Information about the current status of this document, any errata,
   and how to provide feedback on it may be obtained at

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   Copyright (c) 2023 IETF Trust and the persons identified as the
   document authors.  All rights reserved.

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   Trust Legal Provisions and are provided without warranty as described
   in the Revised BSD License.

Table of Contents

   1.  Introduction
   2.  Terminology
   3.  A Functional Architecture
     3.1.  Translating a Service Instance Configuration into an
           Assurance Graph
       3.1.1.  Circular Dependencies
     3.2.  Intent and Assurance Graph
     3.3.  Subservices
     3.4.  Building the Expression Graph from the Assurance Graph
     3.5.  Open Interfaces with YANG Modules
     3.6.  Handling Maintenance Windows
     3.7.  Flexible Functional Architecture
     3.8.  Time Window for Symptoms' History
     3.9.  New Assurance Graph Generation
   4.  IANA Considerations
   5.  Security Considerations
   6.  References
     6.1.  Normative References
     6.2.  Informative References
   Authors' Addresses

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 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 fuel that task, the industry has been standardizing
   on telemetry to push network element performance information (e.g.,

   A network administrator needs to monitor its 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
   (e.g., MIB modules, YANG data models [RFC7950], IP Flow Information
   Export (IPFIX) information elements [RFC7011], syslog plain text
   [RFC5424], Terminal Access Controller Access-Control System Plus
   (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 data models
   used for configuration.  Model-driven telemetry greatly facilitates
   the notion of closed-loop automation, whereby events and updated
   operational states streamed from the network drive remediation change
   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 Multipath (ECMP) bundle?", or "Is that
   issue actually impacting any other customers?"  This task usually
   falls under the so-called "Service Impact Analysis" functional block.

   This document defines 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 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 top 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 the assurance graph automatically 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
   Service-Level Agreement (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 components
   (Section 3.7).

   The architecture presented in this document is implemented by a set
   of YANG modules defined in a companion document [RFC9418].  These
   YANG modules properly define the interfaces between the various
   components of the architecture to foster interoperability.

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" [RFC9315].

   Subservice:  A part or functionality of the network system that can
      be independently assured as a single entity in an 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 relation.

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

   DAG:  Directed Acyclic Graph.

   ECMP:  Equal-Cost Multipath.

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

      Subservice expressions:
         An expression graph representing all the computations to
         execute for a subservice.

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

      Global computation graph:
         An expression graph representing all the computations to
         execute for all services instances (i.e., all computations

   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:  The actual way of retrieving a metric from a
      network element.

   Network Service YANG Module:  The characteristics of a service, as
      agreed upon with consumers of that service [RFC8199].

   Service orchestrator:  "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 process" [RFC8199].

   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:  The 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:  An integer ranging from 0 to 100 that indicates 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 expected, and the special value -1 can be used to
      specify that no value could be computed for that health score, for
      instance, if some metric needed for that computation could not be

   Strongly connected component:  A 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

   Symptom:  A 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 exact meaning of the
   health score value is out of scope of this document.  However, the
   following constraints should be followed: the higher the score, the
   better the service health is and the two extrema are 0 meaning the
   service is completely broken, and 100 meaning the service is
   completely operational.

   The SAIN architecture is a generic architecture, which generates an
   assurance graph from service instance(s), as specified in
   Section 3.1.  This architecture is applicable to not only multiple
   environments (e.g., wireline and 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 and orchestrators 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 layer 2
   virtual private network (L2VPN).  [RFC8466] specifies the parameters
   for such a service.  Examples of symptoms might be symptoms reported
   by specific subservices, including "Interface has high error rate",
   "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 that
   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
   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
   (e.g., an occurrence of a switchover from primary to backup path) and
   forwards the information to the orchestrator, which reconfigures the
   agents.  Finally, the automation loop is closed by having the SAIN
   collector provide 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 module in
   a companion document [RFC9418].  In Figure 1, the communications that
   are normalized by this YANG module are tagged with a "Y".  The use of
   this YANG module is further explained in Section 3.5.

        | 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

   *  Stream (via telemetry, such as YANG-Push [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 the 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, which can be represented as a graph (called an
      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 optic's 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, i.e., the service or subservice at the source of the
   edge depends on the service or subservice at the destination of the

   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
   instance depends on the "peer1" and "peer2" tunnel interfaces (the
   tunnel interfaces created on the peer1 and peer2 devices,
   respectively), 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 the routing protocol from IS-IS to OSPF would
   change the assurance graph accordingly.  As a second example, assume
   that the ECMP is in place for the source router for that specific
   tunnel; in that case, multiple interfaces must now be monitored, in
   addition to monitoring the ECMP health itself.

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 would
   likely model that as the network service depending 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 modeled 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 modeling or alignment between the
   different teams (Engineers A and B) remove the circular dependency.

   As a more elaborate solution, we could consider a graph

   *  Decompose the graph into strongly connected components.

   *  For each strongly connected component:

      -  remove all edges between nodes of the strongly connected

      -  add a new "synthetic" node for the strongly connected

      -  for each edge pointing to a node in the strongly connected
         component, change the destination to the "synthetic" node; and

      -  add a dependency from the "synthetic" 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 connected components 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, node a depends on all these nodes as well, but the
   circular dependency has been removed.

         +---+    +---+          |                +---+    +---+
         | a |    | b |          |                | a |    | b |
         +---+    +---+          |                +---+    +---+
           |        |            |                  |        |
           v        v            |                  v        v
         +---+    +---+          |                +------------+
         | c |--->| d |          |                |  synthetic |
         +---+    +---+          |                +------------+
           ^        |            |               /   |      |   \
           |        |            |              /    |      |    \
           |        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

   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 the 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 the Assurance Graph from Engineer B

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

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

              Figure 6: Merging Graphs from Engineers A and B

   The solution presented above would result in a graph looking as
   follows, where a new "synthetic" 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 since they were causing the circular dependency.

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

       Figure 7: Removing Circular Dependencies after Merging Graphs
                           from Engineers A and B

3.2.  Intent and Assurance Graph

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

   *  Try to capture the intent of the service instance, i.e., What is
      the service instance trying to achieve?  At a minimum, 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 the configuration
   pushed to various devices of a service instance and produce the
   assurance graph for that service instance.

   To schematize what a SAIN orchestrator does, assume that a service
   instance touches two devices and configures a virtual tunnel
   interface on each device.  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 operational.  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 (e.g.,
      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 (i.e., ensure that
   existing subservices are reused as much as possible and avoid
   circular dependencies) is out-of-scope for this document.

   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 Figure 1 makes a distinction between
   the SAIN orchestrator and a different component providing the service
   instance configuration, in practice those two components are most
   likely combined.  The internals of the orchestrator are out of scope
   of this document.

3.3.  Subservices

   A subservice corresponds to a 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 is associated to its assurance,
   which 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 a parameter and 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 identifies the object
   (see examples from the previous paragraph).  Parameters cannot change
   during the life cycle 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
   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, a so-called global computation graph is
   derived.  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
   expressions are combined depends on the dependency types (impacting
   or informational).  Finally, the global computation graph is built by
   combining the service expressions to get a global view of all
   subservices.  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:
      The 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:
      The type of dependency whose health score impacts the health 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 types presented here is not exhaustive.  More
   specific dependency types can be defined by extending the YANG
   module.  For instance, a connectivity subservice depending on several
   path subservices is 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 (e.g.,
   IETF or 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 nonstandard YANG data models, MIB
   modules, the 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
   characteristics (i.e., model, OS version, etc.) of the device from
   which the metrics are fetched.  This metric engine is included in the
   SAIN agent.

3.5.  Open Interfaces with YANG Modules

   The interfaces between the architecture components are open thanks to
   the YANG modules specified in [RFC9418]; 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 are probably the consequence of the maintenance.

   The ietf-service-assurance model described in [RFC9418] enables
   flagging subservices as under maintenance and, in that case, requires
   a string that identifies the person or process that 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.

   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.

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 the two components are most likely combined.  Similarly, the
   SAIN agents are displayed in Figure 1 as being separate components.
   In practice, 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 defined 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
      subservices for a device.

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

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

   *  A service function can be considered 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
   subgraphs from the different agents and gluing them together.  As an
   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 they are outdated and no longer relevant
   or simply because the SAIN agent needs to free up some space.
   Regardless of the reason, it's important for a SAIN collector
   connecting/reconnecting 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 as a result of resolving maintenance issues.
   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).

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

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

4.  IANA Considerations

   This document has no IANA actions.

5.  Security Considerations

   The SAIN architecture helps operators to reduce the mean time to
   detect and the mean time to repair.  However, the SAIN agents must be
   secured; a compromised SAIN agent may be sending incorrect root
   causes or symptoms to the management systems.  Securing the agents
   falls back to ensuring the integrity and confidentiality of the
   assurance graph.  This can be partially achieved by correctly setting
   permissions of each node in the YANG data model, as described in
   Section 6 of [RFC9418].

   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 the Network Configuration Protocol
   (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 telemetry.

   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 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, which is 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 lying.

   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

6.  References

6.1.  Normative References

   [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, <https://www.rfc-editor.org/info/rfc8969>.

   [RFC9418]  Claise, B., Quilbeuf, J., Lucente, P., Fasano, P., and T.
              Arumugam, "A YANG Data Model for Service Assurance",
              RFC 9418, DOI 10.17487/RFC9418, July 2023,

6.2.  Informative References

              "OpenConfig", <https://openconfig.net>.

              Piovesan, A. and E. Griffor, "7 - Reasoning About Safety
              and Security: The Logic of Assurance",
              DOI 10.1016/B978-0-12-803773-7.00007-3, 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,

   [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, <https://www.rfc-editor.org/info/rfc8199>.

   [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, <https://www.rfc-editor.org/info/rfc8466>.

   [RFC8641]  Clemm, A. and E. Voit, "Subscription to YANG Notifications
              for Datastore Updates", RFC 8641, DOI 10.17487/RFC8641,
              September 2019, <https://www.rfc-editor.org/info/rfc8641>.

   [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,

   [RFC9315]  Clemm, A., Ciavaglia, L., Granville, L. Z., and J.
              Tantsura, "Intent-Based Networking - Concepts and
              Definitions", RFC 9315, DOI 10.17487/RFC9315, October
              2022, <https://www.rfc-editor.org/info/rfc9315>.

   [RFC9375]  Wu, B., Ed., Wu, Q., Ed., Boucadair, M., Ed., Gonzalez de
              Dios, O., and B. Wen, "A YANG Data Model for Network and
              VPN Service Performance Monitoring", RFC 9375,
              DOI 10.17487/RFC9375, April 2023,


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


   *  Youssef El Fathi

   *  Éric Vyncke

Authors' Addresses

   Benoit Claise
   Email: benoit.claise@huawei.com

   Jean Quilbeuf
   Email: jean.quilbeuf@huawei.com

   Diego R. Lopez
   Telefonica I+D
   Don Ramon de la Cruz, 82
   28006 Madrid
   Email: diego.r.lopez@telefonica.com

   Dan Voyer
   Bell Canada
   Email: daniel.voyer@bell.ca

   Thangavelu Arumugam
   Milpitas, California
   United States of America
   Email: thangavelu@yahoo.com