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
Consultant
July 2023
Service Assurance for Intent-Based Networking Architecture
Abstract
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
https://www.rfc-editor.org/info/rfc9417.
Copyright Notice
Copyright (c) 2023 IETF Trust and the persons identified as the
document authors. All rights reserved.
This document is subject to BCP 78 and the IETF Trust's Legal
Provisions Relating to IETF Documents
(https://trustee.ietf.org/license-info) in effect on the date of
publication of this document. Please review these documents
carefully, as they describe your rights and restrictions with respect
to this document. Code Components extracted from this document must
include 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. 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
Acknowledgements
Contributors
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.,
[RFC9375]).
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
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: 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
collected.
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
component.
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
status.
* 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
Graph
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
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
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
transformation:
* Decompose the graph into strongly connected components.
* For each strongly connected component:
- remove all edges between nodes of the strongly connected
component;
- add a new "synthetic" node for the strongly connected
component;
- 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 |
+------------+
|
v
+------------+
| 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
dependency:
+------------+ +------------+
| 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
configuration.
* 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
[RFC9418].
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
cloud.
* 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
occurring.
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
causes.
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
results.
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,
<https://www.rfc-editor.org/info/rfc8309>.
[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,
<https://www.rfc-editor.org/info/rfc9418>.
6.2. Informative References
[OpenConfig]
"OpenConfig", <https://openconfig.net>.
[Piovesan2017]
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,
<https://doi.org/10.1016/B978-0-12-803773-7.00007-3>.
[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,
<https://www.rfc-editor.org/info/rfc2865>.
[RFC5424] Gerhards, R., "The Syslog Protocol", RFC 5424,
DOI 10.17487/RFC5424, March 2009,
<https://www.rfc-editor.org/info/rfc5424>.
[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,
<https://www.rfc-editor.org/info/rfc5905>.
[RFC6242] Wasserman, M., "Using the NETCONF Protocol over Secure
Shell (SSH)", RFC 6242, DOI 10.17487/RFC6242, June 2011,
<https://www.rfc-editor.org/info/rfc6242>.
[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,
<https://www.rfc-editor.org/info/rfc7011>.
[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,
<https://www.rfc-editor.org/info/rfc7149>.
[RFC7665] Halpern, J., Ed. and C. Pignataro, Ed., "Service Function
Chaining (SFC) Architecture", RFC 7665,
DOI 10.17487/RFC7665, October 2015,
<https://www.rfc-editor.org/info/rfc7665>.
[RFC7950] Bjorklund, M., Ed., "The YANG 1.1 Data Modeling Language",
RFC 7950, DOI 10.17487/RFC7950, August 2016,
<https://www.rfc-editor.org/info/rfc7950>.
[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,
<https://www.rfc-editor.org/info/rfc8446>.
[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,
<https://www.rfc-editor.org/info/rfc8907>.
[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,
<https://www.rfc-editor.org/info/rfc9375>.
Acknowledgements
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.
Contributors
* Youssef El Fathi
* Éric Vyncke
Authors' Addresses
Benoit Claise
Huawei
Email: benoit.claise@huawei.com
Jean Quilbeuf
Huawei
Email: jean.quilbeuf@huawei.com
Diego R. Lopez
Telefonica I+D
Don Ramon de la Cruz, 82
28006 Madrid
Spain
Email: diego.r.lopez@telefonica.com
Dan Voyer
Bell Canada
Canada
Email: daniel.voyer@bell.ca
Thangavelu Arumugam
Consultant
Milpitas, California
United States of America
Email: thangavelu@yahoo.com
