Service Assurance for Intent-based Networking Architecture
draft-ietf-opsawg-service-assurance-architecture-03
The information below is for an old version of the document.
| Document | Type |
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|---|---|---|---|
| Authors | Benoît Claise , Jean Quilbeuf , Diego Lopez , Daniel Voyer , Thangam Arumugam | ||
| Last updated | 2022-06-08 (Latest revision 2022-03-07) | ||
| Replaces | draft-claise-opsawg-service-assurance-architecture | ||
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draft-ietf-opsawg-service-assurance-architecture-03
OPSAWG B. Claise
Internet-Draft J. Quilbeuf
Intended status: Informational Huawei
Expires: 8 September 2022 D. Lopez
Telefonica I+D
D. Voyer
Bell Canada
T. Arumugam
Cisco Systems, Inc.
7 March 2022
Service Assurance for Intent-based Networking Architecture
draft-ietf-opsawg-service-assurance-architecture-03
Abstract
This document describes an architecture for Service Assurance for
Intent-based Networking (SAIN). This architecture aims at assuring
that service instances are running as expected. As services rely
upon multiple sub-services provided by 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
the network root cause but also the impacted services when a network
component fails or degrades.
Status of This Memo
This Internet-Draft is submitted in full conformance with the
provisions of BCP 78 and BCP 79.
Internet-Drafts are working documents of the Internet Engineering
Task Force (IETF). Note that other groups may also distribute
working documents as Internet-Drafts. The list of current Internet-
Drafts is at https://datatracker.ietf.org/drafts/current/.
Internet-Drafts are draft documents valid for a maximum of six months
and may be updated, replaced, or obsoleted by other documents at any
time. It is inappropriate to use Internet-Drafts as reference
material or to cite them other than as "work in progress."
This Internet-Draft will expire on 8 September 2022.
Copyright Notice
Copyright (c) 2022 IETF Trust and the persons identified as the
document authors. All rights reserved.
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This document is subject to BCP 78 and the IETF Trust's Legal
Provisions Relating to IETF Documents (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. Terminology . . . . . . . . . . . . . . . . . . . . . . . . . 2
2. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 5
3. Architecture . . . . . . . . . . . . . . . . . . . . . . . . 7
3.1. Inferring a Service Instance Configuration into an
Assurance Graph . . . . . . . . . . . . . . . . . . . . 10
3.1.1. Circular Dependencies . . . . . . . . . . . . . . . . 12
3.2. Intent and Assurance Graph . . . . . . . . . . . . . . . 16
3.3. Subservices . . . . . . . . . . . . . . . . . . . . . . . 16
3.4. Building the Expression Graph from the Assurance Graph . 17
3.5. Building the Expression from a Subservice . . . . . . . . 18
3.6. Open Interfaces with YANG Modules . . . . . . . . . . . . 18
3.7. Handling Maintenance Windows . . . . . . . . . . . . . . 18
3.8. Flexible Architecture . . . . . . . . . . . . . . . . . . 19
3.9. Timing . . . . . . . . . . . . . . . . . . . . . . . . . 20
3.10. New Assurance Graph Generation . . . . . . . . . . . . . 21
4. Security Considerations . . . . . . . . . . . . . . . . . . . 21
5. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 22
6. Contributors . . . . . . . . . . . . . . . . . . . . . . . . 22
7. Open Issues . . . . . . . . . . . . . . . . . . . . . . . . . 22
8. References . . . . . . . . . . . . . . . . . . . . . . . . . 22
8.1. Normative References . . . . . . . . . . . . . . . . . . 22
8.2. Informative References . . . . . . . . . . . . . . . . . 22
Appendix A. Changes between revisions . . . . . . . . . . . . . 24
Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . 25
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 25
1. Terminology
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
"SHOULD", "SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED", "MAY", and
"OPTIONAL" in this document are to be interpreted as described in BCP
14 [RFC2119] [RFC8174] when, and only when, they appear in all
capitals, as shown here.
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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.
Assurance case: According to [Piovesan2017]: "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."
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
relations.
SAIN collector: A functional component that fetches or receives the
computer-consumable output of the SAIN agent(s) and displays it in a
user friendly form or process it locally.
DAG: Directed Acyclic Graph.
ECMP: Equal Cost Multiple Paths
Expression graph: A generic term for a DAG representing a computation
in SAIN. More specific terms are:
* Subservice expressions: Is an expression graph representing all
the computations to execute for a subservice.
* Service expressions: Is an expression graph representing all the
computations to execute for a service instance, i.e., including
the computations for all dependent subservices.
* Global computation graph: Is an expression graph representing all
the computations to execute for all services instances (i.e., all
computations performed).
Dependency: The directed relationship between subservice instances in
the assurance graph.
Informational Dependency: Type of dependency whose health score does
not impact the health score of its parent subservice or service
instance(s) in the assurance graph. However, the symptoms should be
taken into account in the parent service instance or subservice
instance(s), for informational reasons.
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Impacting Dependency: Type of dependency whose score impacts the
score of its parent subservice or service instance(s) in the
assurance graph. The symptoms are taken into account in the parent
service instance or subservice instance(s), as the impacting reasons.
Metric: An information retrieved from the network running the assured
service.
Metric engine: A functional components that maps metrics to a list of
candidate metric implementations depending on the network element.
Metric implementation: Actual way of retrieving a metric from a
network element.
Network service YANG module: describes the characteristics of a
service as agreed upon with consumers of that service [RFC8199].
Service instance: A specific instance of a service.
Service configuration orchestrator: Quoting RFC8199, "Network Service
YANG Modules describe the characteristics of a service, as agreed
upon with consumers of that service. That is, a service module does
not expose the detailed configuration parameters of all participating
network elements and features but describes an abstract model that
allows instances of the service to be decomposed into instance data
according to the Network Element YANG Modules of the participating
network elements. The service-to-element decomposition is a separate
process; the details depend on how the network operator chooses to
realize the service. For the purpose of this document, the term
"orchestrator" is used to describe a system implementing such a
process."
SAIN orchestrator: A functional component that is in charge of
fetching the configuration specific to each service instance and
converting it into an assurance graph.
Health status: Score and symptoms indicating whether a service
instance or a subservice is "healthy". A non-maximal score must
always be explained by one or more symptoms.
Health score: Integer ranging from 0 to 100 indicating the health of
a subservice. A score of 0 means that the subservice is broken, a
score of 100 means that the subservice in question is operating as
expected.
Subservice: Part or functionality of the network system that can be
independently assured as a single entity in assurance graph.
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Strongly connected component: subset of a directed graph such that
there is a (directed) path from any node of the subset to any other
node. A DAG does not contain any strongly connected component.
Symptom: Reason explaining why a service instance or a subservice is
not completely healthy.
2. 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.
Quoting RFC8199: "Network Service YANG Modules describe the
characteristics of a service, as agreed upon with consumers of that
service. That is, a service module does not expose the detailed
configuration parameters of all participating network elements and
features but describes an abstract model that allows instances of the
service to be decomposed into instance data according to the Network
Element YANG Modules of the participating network elements. The
service-to-element decomposition is a separate process; the details
depend on how the network operator chooses to realize the service.
For the purpose of this document, the term "orchestrator" is used to
describe a system implementing such a process."
Service configuration orchestrators deploy Network Service YANG
Modules [RFC8199] that will infer network-wide configuration and,
therefore the configuration of the appropriate device modules
(Section 3 of [RFC8969]). Network configuration is based on these
device YANG modules, with protocol/encoding such as NETCONF/XML
[RFC6241] , RESTCONF/JSON [RFC8040], gNMI/gRPC/protobuf, etc.
Knowing that a configuration is applied doesn't imply that the
service is running as expected (e.g., the service might be degraded
because of a failure in the network), the network operator must
monitor the service operational data at the same time as the
configuration (Section 3.3 of [RFC8969]. The industry has been
standardizing on telemetry to push network element performance
information.
A network administrator needs to monitor her network and services as
a whole, independently of the use cases or 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 protocols, the network management
must perform the difficult and time-consuming job of mapping data
models: the model used for configuration with the model used for
monitoring. This problem is compounded by a large, disparate set of
data sources (MIB modules, YANG models [RFC7950], IPFIX information
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elements [RFC7011], syslog plain text [RFC3164], TACACS+ [RFC8907],
RADIUS [RFC2865], etc.). In order to avoid this data model mapping,
the industry converged on model-driven telemetry to stream the
service operational data, reusing the YANG models used for
configuration. Model-driven telemetry greatly facilitates the notion
of closed-loop automation whereby events/status from the network
drive remediation changes back into the network.
However, it proves difficult for network operators to correlate the
service degradation with the network root cause. For example, why
does my L3VPN fail to connect? Why is this specific service slow?
The reverse, i.e., which services are impacted when a network
component fails or degrades, is even more interesting for the
operators. For example, which services are impacted when this
specific optic dBM begins to degrade? Which applications are
impacted by this ECMP imbalance? Is that issue actually impacting
any other customers?
Intent-based approaches are often declarative, starting from a
statement of "The service works as expected" and trying to enforce
it. Such approaches are mainly suited for greenfield deployments.
Aligned with Section 3.3 of [RFC7149], and instead of approaching
intent from a declarative way, 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 service definition and from the network
configuration. In some cases, the assurance graph may also be
explicitly completed to add an intent not exposed in the service
model itself (e.g. the service must rely on a backup physical path).
This assurance graph is decomposed into components, which are then
assured independently. The root of the assurance graph represents
the service to assure, and its children represent components
identified as its direct dependencies; each component can have
dependencies as well. The SAIN architecture updates the assurance
graph when services are modified or when the network conditions
change.
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When a service is degraded, the SAIN architecture will highlight, to
the best of its knowledge, where in the assurance service graph to
look, as opposed to going hop by hop to troubleshoot the issue. Not
only can this architecture help to correlate service degradation with
network root cause/symptoms, but it can deduce from the assurance
graph the number and type of services 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 should focus his priority on the degrading/failing
components impacting the highest number customers, especially the
ones with the SLA contracts involving penalties in case of failure.
This architecture provides the building blocks to assure both
physical and virtual entities and is flexible with respect to
services and subservices, of (distributed) graphs, and of components
(Section 3.8).
3. Architecture
The goal of SAIN is to assure that service instances are operating
correctly and if not, to pinpoint what is wrong. More precisely,
SAIN computes a score for each service instance and outputs symptoms
explaining that score, especially why the score is not maximal. The
score augmented with the symptoms is called the health status.
The SAIN architecture is a generic architecture, applicable to
multiple environments. Obviously wireline but also wireless, but
also different domains such as 5G, NFV domain with a virtual
infrastructure manager (VIM), etc. And as already noted, for
physical or virtual devices, as well as virtual functions. Thanks to
the distributed graph design principle, graphs from different
environments/orchestrator can be combined together.
As an example of a service, let us consider a point-to-point L2VPN
connection (i.e., pseudowire). Such a service would take as
parameters the two ends of the connection (device, interface or
subinterface, and address of the other end) and configure both
devices (and maybe more) so that a L2VPN connection is established
between the two devices. Examples of symptoms might be "Interface
has high error rate" or "Interface flapping", or "Device almost out
of memory".
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To compute the health status of such a service, the service
definition is decomposed into an assurance graph formed by
subservices linked through dependencies. Each subservice is then
turned into an expression graph that details how to fetch metrics
from the devices and compute the health status of the subservice.
The subservice expressions are combined according to the dependencies
between the subservices in order to obtain the expression graph which
computes the health status of the service.
The overall SAIN architecture is presented in Figure 1. Based on the
service configuration, the SAIN orchestrator decomposes the assurance
graph, to the best of its knowledge. It then sends to the SAIN
agents the assurance graph along 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. Finally, the
automation loop is closed by having the SAIN collector providing
feedback to the network/service orchestrator.
In order to make agents, orchestrators and collectors from different
vendors interoperable, their interface is defined as a YANG model in
a companion RFC [I-D.ietf-opsawg-service-assurance-yang]. In
Figure 1, the communications that are normalized by this model are
tagged with a "Y". The use of these YANG modules is further
explained in Section 3.6.
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+-----------------+
| Service |
| Configuration |<--------------------+
| Orchestrator | |
+-----------------+ |
| | |
| | Network |
| | Service | Feedback
| | Instance | Loop
| | Configuration |
| | |
| V |
| +-----------------+ +-------------------+
| | SAIN | | SAIN |
| | Orchestrator | | Collector |
| +-----------------+ +-------------------+
| | ^
| Y| Configuration | Health Status
| | (assurance graph) Y| (Score + Symptoms)
| V | Streamed
| +-------------------+ | via Telemetry
| |+-------------------+ |
| ||+-------------------+ |
| +|| SAIN |---------+
| +| agent |
| +-------------------+
| ^ ^ ^
| | | |
| | | | Metric Collection
V V V V
+-------------------------------------------------------------+
| Monitored Entities |
| |
+-------------------------------------------------------------+
Figure 1: SAIN Architecture
In order to produce the score assigned to a service instance, the
architecture performs the following tasks:
* Analyze the configuration pushed to the network device(s) for
configuring the service instance and decide: which information is
needed from the device(s), such a piece of information being
called a metric, which operations to apply to the metrics for
computing the health status.
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* Stream (via telemetry [RFC8641]) operational and config metric
values when possible, else continuously poll.
* Continuously compute the health status of the service instances,
based on the metric values.
3.1. Inferring a Service Instance Configuration into an Assurance Graph
In order to structure the assurance of a service instance, the
service instance is decomposed 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 this
architecture, for the following reasons.
* The result of this decomposition provides a relational picture of
a service instance, that can be represented as a graph (called
assurance graph) to the operator.
* Subservices provide a scope for particular expertise and thereby
enable contribution from external experts. For instance, the
subservice dealing with the optics health should be reviewed and
extended by an expert in optical interfaces.
* Subservices that are common to several service instances are
reused for reducing the amount of computation needed.
The assurance graph of a service instance is a DAG representing the
structure of the assurance case for the service instance. The nodes
of this graph are service instances or subservice instances. Each
edge of this graph indicates a dependency between the two nodes at
its extremities: the service or subservice at the source of the edge
depends on the service or subservice at the destination of the edge.
Figure 2 depicts a simplistic example of the assurance graph for a
tunnel service. The node at the top is the service instance, the
nodes below are its dependencies. In the example, the tunnel service
instance depends on the "peer1" and "peer2" tunnel interfaces, which
in turn depend on the respective physical interfaces, which finally
depend on the respective "peer1" and "peer2" devices. The tunnel
service instance also depends on the IP connectivity that depends on
the IS-IS routing protocol.
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+------------------+
| 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 be reflected in the assurance graph. As a first example, a
change of routing protocol from IS-IS to OSPF would change the
assurance graph accordingly. As a second example, assuming that ECMP
is in place for the source router for that specific tunnel; in that
case, multiple interfaces must now be monitored, on top of the
monitoring the ECMP health itself.
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3.1.1. Circular Dependencies
The edges of the assurance graph represent dependencies. An
assurance graph is a DAG if and only if there are no circular
dependencies among the subservices, and every assurance graph should
avoid circular dependencies. However, in some cases, circular
dependencies might appear in the assurance graph.
First, the assurance graph of a whole system is obtained by combining
the assurance graph of every service running on that system. Here
combining means that two subservices having the same type and the
same parameters are in fact the same subservice and thus a single
node in the graph. For instance, the subservice of type "device"
with the only parameter (the device id) set to "PE1" will appear only
once in the whole assurance graph even if several services 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 kubernetes cluster. If we represent the cluster by a
subservice and the network service by another subservice, we will
likely model that the network service depends on the cluster, because
the network service is orchestrated by kubernetes, and that the
cluster depends on the network service because it implements the
communications. A finer decomposition might distinguish between the
resources for executing containers (a part of our cluster subservice)
and the communication between the containers (which could be modelled
in the same way as communication between routers).
In any case, it is likely that circular dependencies will show up in
the assurance graph. A first step would be to detect circular
dependencies as soon as possible in the SAIN architecture. Such a
detection could be carried out by the SAIN Orchestrator. Whenever a
circular dependency is detected, the newly added service would not be
monitored until more careful modelling or alignment between the
different teams (engineer A and B) remove the circular dependency.
As more elaborate solution we could consider a graph transformation:
* Decompose the graph into strongly connected components.
* For each strongly connected component:
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- Remove all edges between nodes of the strongly connected
component
- Add a new "top" node for the strongly connected component
- For each edge pointing to a node in the strongly connected
component, change the destination to the "top" node
- Add a dependency from the top node to every node in the
strongly connected component.
Such an algorithm would include all symptoms detected by any
subservice in one of the strongly component and make it available to
any subservice that depends on it. Figure 3 shows an example of such
a transformation. On the left-hand side, the nodes c, d, e and f
form a strongly connected component. The status of a should depend
on the status of c, d, e, f, g, and h, but this is hard to compute
because of the circular dependency. On the right hand-side, a
depends on all this nodes as well, but there the circular dependency
has been removed.
+---+ +---+ | +---+ +---+
| a | | b | | | a | | b |
+---+ +---+ | +---+ +---+
| | | | |
v v | v v
+---+ +---+ | +------------+
| c |--->| d | | | top |
+---+ +---+ | +------------+
^ | | / | | \
| | | / | | \
| v | v v v v
+---+ +---+ | +---+ +---+ +---+ +---+
| f |<---| e | | | f | | c | | d | | e |
+---+ +---+ | +---+ +---+ +---+ +---+
| | | | |
v v | v v
+---+ +---+ | +---+ +---+
| g | | h | | | g | | h |
+---+ +---+ | +---+ +---+
Before After
Transformation Transformation
Figure 3: Graph transformation
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We consider a concrete example to illustrate this transformation.
Let's assume that Engineer A is building an assurance graph dealing
with IS-IS and Engineer B is building an assurance graph dealing with
OSPF. The graph from Engineer A could contain the following:
+------------+
| IS-IS Link |
+------------+
|
v
+------------+
| Phys. Link |
+------------+
| |
v v
+-------------+ +-------------+
| Interface 1 | | Interface 2 |
+-------------+ +-------------+
Figure 4: Fragment of assurance graph from Engineer A
The graph from Engineer B could contain the following:
+------------+
| OSPF Link |
+------------+
| | |
v | v
+-------------+ | +-------------+
| Interface 1 | | | Interface 2 |
+-------------+ | +-------------+
| | |
v v v
+------------+
| Phys. Link |
+------------+
Figure 5: Fragment of assurance graph from Engineer B
Each Interface subservice and the Physical Link subservice are common
two the both fragment above. Each of these subservice 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:
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+------------+ +------------+
| IS-IS Link | | OSPF Link |---+
+------------+ +------------+ |
| | | |
| +-------- + | |
v v | |
+------------+ | |
| Phys. Link |<-------+ | |
+------------+ | | |
| ^ | | | |
| | +-------+ | | |
v | v | v |
+-------------+ +-------------+ |
| Interface 1 | | Interface 2 | |
+-------------+ +-------------+ |
^ |
| |
+------------------------------+
Figure 6: Merging graphs from A and B
The solution presented above would result in graph looking as
follows, where a new "empty" node is included. Using that
transformation, all dependencies are indirectly satisfied for the
nodes outside the circular dependency, in the sense that both IS-IS
and OSPF links have indirect dependencies to the two interfaces and
the link. However, the dependencies between the link and the
interfaces are lost as they were causing the circular dependency.
+------------+ +------------+
| IS-IS Link | | OSPF Link |
+------------+ +------------+
| |
v v
+------------+
| |
+------------+
|
+-----------+-------------+
| | |
v v v
+-------------+ +------------+ +-------------+
| Interface 1 | | Phys. Link | | Interface 2 |
+-------------+ +------------+ +-------------+
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Figure 7: Removing circular dependencies after merging graphs
from A and B
3.2. Intent and Assurance Graph
The SAIN orchestrator analyzes the configuration of a service
instance to:
* Try to capture the intent of the service instance, i.e., what is
the service instance trying to achieve.
* Decompose the service instance into subservices representing the
network features on which the service instance relies.
The SAIN orchestrator must be able to analyze configuration from
various devices and produce the assurance graph.
To schematize what a SAIN orchestrator does, assume that the
configuration for a service instance touches two devices and
configure on each device a virtual tunnel interface. Then:
* Capturing the intent would start by detecting that the service
instance is actually a tunnel between the two devices, and stating
that this tunnel must be functional. This is the current state of
SAIN, however it does not completely capture the intent which
might additionally include, for instance, the latency and
bandwidth requirements of this tunnel.
* Decomposing the service instance into subservices would result in
the assurance graph depicted in Figure 2, for instance.
In order for SAIN to be applied, the configuration necessary for each
service instance should be identifiable and thus should come from a
"service-aware" source. While the Figure 1 makes a distinction
between the SAIN orchestrator and a different component providing the
service instance configuration, in practice those two components are
mostly likely combined. The internals of the orchestrator are
currently out of scope of this document.
3.3. Subservices
A subservice corresponds to subpart or a feature of the network
system that is needed for a service instance to function properly.
In the context of SAIN, subservice is actually a shortcut for
subservice assurance, that is the method for assuring that a
subservice behaves correctly.
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Subservices, just as with services, have high-level parameters that
specify the type and specific instance to be assured. For example,
assuring a device requires the specific deviceId as parameter. For
example, assuring an interface requires the specific combination of
deviceId and interfaceId.
A subservice is also characterized by a list of metrics to fetch and
a list of computations to apply to these metrics in order to infer a
health status.
3.4. Building the Expression Graph from the Assurance Graph
From the assurance graph is derived a so-called global computation
graph. First, each subservice instance is transformed into a set of
subservice expressions that take metrics and constants as input
(i.e., sources of the DAG) and produce the status of the subservice,
based on some heuristics. 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. In other words, the global
computation graph encodes all the operations needed to produce health
statuses from the collected metrics.
Subservices shall be device independent. To justify this, let's
consider the interface operational status. Depending on the device
capabilities, this status can be collected by an industry-accepted
YANG module (IETF, Openconfig), by a vendor-specific YANG module, or
even by a MIB module. If the subservice was dependent on the
mechanism to collect the operational status, then we would need
multiple subservice definitions in order to support all different
mechanisms. This also implies that, while waiting for all the
metrics to be available via standard YANG modules, SAIN agents might
have to retrieve metric values via non-standard YANG models, via MIB
modules, Command Line Interface (CLI), etc., effectively implementing
a normalization layer between data models and information models.
In order to keep subservices independent from 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 (model, OS version, etc.) of the device from which
the metrics are fetched.
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3.5. Building the Expression from a Subservice
Additionally, to the list of metrics, each subservice defines a list
of expressions to apply on the metrics in order to compute the health
status of the subservice. The definition or the standardization of
those expressions (also known as heuristic) is currently out of scope
of this standardization.
3.6. Open Interfaces with YANG Modules
The interfaces between the architecture components are open thanks to
the YANG modules specified in YANG Modules for Service Assurance
[I-D.ietf-opsawg-service-assurance-yang]; they specify objects for
assuring network services based on their decomposition into so-called
subservices, according to the SAIN architecture.
This module is 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
[I-D.ietf-opsawg-service-assurance-yang].
3.7. Handling Maintenance Windows
Whenever network components are under maintenance, the operator want
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, as well as symptoms related to the device-
specific subservices, such as the interfaces, as their state changes
is probably the consequence of the maintenance.
To configure network components as "under maintenance" in the SAIN
architecture, the ietf-service-assurance model proposed in
[I-D.ietf-opsawg-service-assurance-yang] specifies an "under-
maintenance" flag per service or subservice instance. When this flag
is set and only when this flag is set, the companion field
"maintenance-contact" must be set to a string that identifies the
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person or process who requested the maintenance. When a service or
subservice is flagged as under maintenance, it may 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 sets the "under-maintenance" flag for the subservice
"Peer1" device. This inhibits the emission of symptoms 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 sets the "under-maintenance" flag for the subservice
"Peer1 Physical Interface". This inhibits the emission of
symptoms 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 sets the "under-maintenance" flag
for the subservice "IS-IS Routing Protocol". This inhibits the
emission of symptoms from "IP connectivity" and "Tunnel Service
Instance". All other subservices are unaffected.
3.8. Flexible Architecture
The SAIN architecture is flexible in terms of components. While the
SAIN architecture in Figure 1 makes a distinction between two
components, the SAIN configuration orchestrator and the SAIN
orchestrator, in practice those two components are mostly likely
combined. Similarly, the SAIN agents are displayed in Figure 1 as
being separate components. Practically, the SAIN agents could be
either independent components or directly integrated in monitored
entities. A practical example is an agent in a router.
The SAIN architecture is also flexible in terms of services and
subservices. Most examples in this document deal with the notion of
Network Service YANG modules, with well-known service such as L2VPN
or tunnels. However, the concepts of services is general enough to
cross into different domains. One of them is the domain of service
management on network elements, with also requires its own assurance.
Examples includes a DHCP server on a Linux server, a data plane, an
IPFIX export, etc. The notion of "service" is generic in this
architecture. Indeed, a configured service can itself be a
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subservice for someone else. Exactly like a DHCP server/ data plane/
IPFIX export can be considered as subservices for a device, exactly
like an routing instance can be considered as a subservice for a
L3VPN, exactly like a tunnel can considered as a subservice for an
application in the cloud. Exactly like a service function can be be
considered as a subservice for a service function chain [RFC7665].
The assurance graph is created to be flexible and open, regardless of
the subservice types, locations, or domains.
The SAIN architecture is also flexible in terms of distributed
graphs. As shown in Figure 1, our 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 constrain. 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 well possible within this
architecture.
3.9. Timing
The SAIN architecture requires time synchronization, with Network
Time Protocol (NTP) [RFC5905] as a candidate, between all elements:
monitored entities, SAIN agents, Service Configuration 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.
The SAIN agent might have to remove some symptoms for specific
subservice symptoms, because there are outdated and not relevant any
longer, or simply because the SAIN agent needs to free up some space.
Regardless of the reason, it's important for a SAIN collector
(re-)connecting to a SAIN agent to understand the effect of this
garbage collection. Therefore, the SAIN agent contains a YANG object
specifying the date and time at which the symptoms history starts for
the subservice instances.
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3.10. New Assurance Graph Generation
The assurance graph will change along the time, because services and
subservices come and go (changing the dependencies between
subservices), or simply because a subservice is now under
maintenance. Therefore an assurance graph version must be
maintained, along with the date and time of its last generation. The
date and time of a particular subservice instance (again dependencies
or under maintenance) might be kept. From a client point of view, an
assurance graph change is triggered by the value of the assurance-
graph-version and assurance-graph-last-change YANG leaves. At that
point in time, the client (collector) follows the following process:
* Keep the previous assurance-graph-last-change value (let's call it
time T)
* Run through all subservice instance and process the subservice
instances for which the last-change is newer that the time T
* Keep the new assurance-graph-last-change as the new referenced
date and time
4. Security Considerations
The SAIN architecture helps operators to reduce the mean time to
detect and mean time to repair. As such, it should not cause any
security threats. However, the SAIN agents must be secure: a
compromised SAIN agents could be sending wrong root causes or
symptoms to the management systems.
Except for the configuration of telemetry, the agents do not need
"write access" to the devices they monitor. This configuration is
applied with a YANG module, whose protection is covered by Secure
Shell (SSH) [RFC6242] for NETCONF or TLS [RFC8446] for RESTCONF.
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.
If a closed loop system relies on this architecture then the well
known issue of those system also applies, i.e., a lying device or
compromised agent could trigger partial reconfiguration of the
service or network. The SAIN architecture neither augments or
reduces this risk.
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5. IANA Considerations
This document includes no request to IANA.
6. Contributors
* Youssef El Fathi
* Eric Vyncke
7. Open Issues
Refer to the Intent-based Networking NMRG documents (Intent
Assurance, Service Intent: synonym for custom service model see
[I-D.irtf-nmrg-ibn-concepts-definitions] and
[I-D.irtf-nmrg-ibn-intent-classification] ).
8. References
8.1. Normative References
[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", BCP 14, RFC 2119,
DOI 10.17487/RFC2119, March 1997,
<https://www.rfc-editor.org/info/rfc2119>.
[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>.
[RFC8174] Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC
2119 Key Words", BCP 14, RFC 8174, DOI 10.17487/RFC8174,
May 2017, <https://www.rfc-editor.org/info/rfc8174>.
8.2. Informative References
[I-D.ietf-opsawg-service-assurance-yang]
Claise, B., Quilbeuf, J., Lucente, P., Fasano, P., and T.
Arumugam, "YANG Modules for Service Assurance", Work in
Progress, Internet-Draft, draft-ietf-opsawg-service-
assurance-yang-02, 4 January 2022,
<https://www.ietf.org/archive/id/draft-ietf-opsawg-
service-assurance-yang-02.txt>.
[I-D.irtf-nmrg-ibn-concepts-definitions]
Clemm, A., Ciavaglia, L., Granville, L. Z., and J.
Tantsura, "Intent-Based Networking - Concepts and
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Definitions", Work in Progress, Internet-Draft, draft-
irtf-nmrg-ibn-concepts-definitions-06, 15 December 2021,
<https://www.ietf.org/archive/id/draft-irtf-nmrg-ibn-
concepts-definitions-06.txt>.
[I-D.irtf-nmrg-ibn-intent-classification]
Li, C., Havel, O., Olariu, A., Martinez-Julia, P., Nobre,
J. C., and D. R. Lopez, "Intent Classification", Work in
Progress, Internet-Draft, draft-irtf-nmrg-ibn-intent-
classification-06, 22 February 2022,
<https://www.ietf.org/archive/id/draft-irtf-nmrg-ibn-
intent-classification-06.txt>.
[Piovesan2017]
Piovesan, A. and E. Griffor, "Reasoning About Safety and
Security: The Logic of Assurance", 2017.
[RFC2865] Rigney, C., Willens, S., Rubens, A., and W. Simpson,
"Remote Authentication Dial In User Service (RADIUS)",
RFC 2865, DOI 10.17487/RFC2865, June 2000,
<https://www.rfc-editor.org/info/rfc2865>.
[RFC3164] Lonvick, C., "The BSD Syslog Protocol", RFC 3164,
DOI 10.17487/RFC3164, August 2001,
<https://www.rfc-editor.org/info/rfc3164>.
[RFC6241] Enns, R., Ed., Bjorklund, M., Ed., Schoenwaelder, J., Ed.,
and A. Bierman, Ed., "Network Configuration Protocol
(NETCONF)", RFC 6241, DOI 10.17487/RFC6241, June 2011,
<https://www.rfc-editor.org/info/rfc6241>.
[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>.
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[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>.
[RFC8040] Bierman, A., Bjorklund, M., and K. Watsen, "RESTCONF
Protocol", RFC 8040, DOI 10.17487/RFC8040, January 2017,
<https://www.rfc-editor.org/info/rfc8040>.
[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>.
[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>.
[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>.
[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>.
[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>.
Appendix A. Changes between revisions
v00 - v01
* Cover the feedback received during the WG call for adoption
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Acknowledgements
The authors would like to thank Stephane Litkowski, Charles Eckel,
Rob Wilton, Vladimir Vassiliev, Gustavo Alburquerque, Stefan Vallin,
Eric Vyncke, and Mohamed Boucadair for their reviews and feedback.
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
Madrid 28006
Spain
Email: diego.r.lopez@telefonica.com
Dan Voyer
Bell Canada
Canada
Email: daniel.voyer@bell.ca
Thangam Arumugam
Cisco Systems, Inc.
Milpitas (California),
United States of America
Email: tarumuga@cisco.com
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