Internet-Draft SAIN Architecture June 2022
Claise, et al. Expires 30 December 2022 [Page]
Workgroup:
OPSAWG
Internet-Draft:
draft-ietf-opsawg-service-assurance-architecture-06
Published:
Intended Status:
Informational
Expires:
Authors:
B. Claise
Huawei
J. Quilbeuf
Huawei
D. Lopez
Telefonica I+D
D. Voyer
Bell Canada
T. Arumugam
Cisco Systems, Inc.

Service Assurance for Intent-based Networking Architecture

Abstract

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

Status of This Memo

This Internet-Draft is submitted in full conformance with the provisions of BCP 78 and BCP 79.

Internet-Drafts are working documents of the Internet Engineering Task Force (IETF). Note that other groups may also distribute working documents as Internet-Drafts. The list of current Internet-Drafts is at 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 30 December 2022.

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Table of Contents

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 service is up and running as expected. For instance, the service might be degraded because of a failure in the network, the experience quality is distorted, 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 operational data at the same time as the configuration (Section 3.3 of [RFC8969]. To feed that task, the industry has been standardizing on telemetry to push network element performance information.

A network administrator needs to monitor their network and services as a whole, independently of the management protocols. With different protocols come different data models, and different ways to model the same type of information. When network administrators deal with multiple management protocols, the network management entities have to perform the difficult and time-consuming job of mapping data models: e.g. the model used for configuration with the model used for monitoring when separate models or protocols are used. This problem is compounded by a large, disparate set of data sources (MIB modules, YANG models [RFC7950], IPFIX information elements [RFC7011], syslog plain text [RFC5424], TACACS+ [RFC8907], RADIUS [RFC2865], etc.). In order to avoid this data model mapping, the industry converged on model-driven telemetry to stream the service operational data, reusing the YANG models used for configuration. Model-driven telemetry greatly facilitates the notion of closed-loop automation whereby events/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?" 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 dBM begins to degrade?", "Which applications are impacted by this ECMP imbalance?", or "Is that issue actually impacting any other customers?". This task usually falls under the so-called "Service Impact Analysis" functional block.

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.

In this document, we propose an architecture implementing Service Assurance for Intent-Based Networking (SAIN). 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 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 [RFC8309]) can also be used to build the assurance graph. 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 upon 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 orchestrator updates automatically the assurance graph when services are 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 a list of symptoms originating from specific 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 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 3.7).

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

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.

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.

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

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

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

DAG: Directed Acyclic Graph.

ECMP: Equal Cost Multiple Paths

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

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

Metric: An information retrieved from the network running the assured service.

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

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

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

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

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

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

3. A Functional Architecture

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

The SAIN architecture is a generic architecture, applicable to multiple environments (e.g. wireline, wireless), but also different domains (e.g. 5G, 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. [RFC8466] specifies the parameters for such a service. Examples of symptoms might be symptoms reported by specific subservices "Interface has high error rate" or "Interface flapping", or "Device almost out of memory" as well as symptoms more specific to the service such as "Site disconnected from VPN".

To compute the health status of 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 provided by the service orchestrator, the SAIN orchestrator decomposes the assurance graph. 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 document [I-D.ietf-opsawg-service-assurance-yang]. In Figure 1, the communications that are normalized by this YANG model are tagged with a "Y". The use of this YANG model is further explained in Section 3.5.


       +-----------------+
       | Service         |
       | 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
      +-------------------------------------------------------------+
      |           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:

3.1. Inferring 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, 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.

                         +------------------+
                         | Tunnel           |
                         | Service Instance |
                         +------------------+
                                   |
              +--------------------+-------------------+
              |                    |                   |
              v                    v                   v
         +-------------+    +--------------+    +-------------+
         | Peer1       |    | IP           |    | Peer2       |
         | Tunnel      |    | Connectivity |    | Tunnel      |
         | Interface   |    |              |    | Interface   |
         +-------------+    +--------------+    +-------------+
                |                  |                  |
                |    +-------------+--------------+   |
                |    |             |              |   |
                v    v             v              v   v
         +-------------+    +-------------+     +-------------+
         | Peer1       |    | IS-IS       |     | Peer2       |
         | Physical    |    | Routing     |     | Physical    |
         | Interface   |    | Protocol    |     | Interface   |
         +-------------+    +-------------+     +-------------+
                |                                     |
                v                                     v
         +-------------+                        +-------------+
         |             |                        |             |
         | Peer1       |                        | Peer2       |
         | Device      |                        | Device      |
         +-------------+                        +-------------+
Figure 2: Assurance Graph Example

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

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:

    • 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 these nodes as well, but there the circular dependency has been removed.

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

         Before                                     After
      Transformation                           Transformation
Figure 3: Graph transformation

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:

The graph from Engineer B could contain the following:

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


      +------------+      +------------+
      | IS-IS Link |      | OSPF Link  |---+
      +------------+      +------------+   |
            |               |     |        |
            |     +-------- +     |        |
            v     v               |        |
      +------------+              |        |
      | Phys. Link |<-------+     |        |
      +------------+        |     |        |
        |  ^     |          |     |        |
        |  |     +-------+  |     |        |
        v  |             v  |     v        |
      +-------------+  +-------------+     |
      | Interface 1 |  | Interface 2 |     |
      +-------------+  +-------------+     |
            ^                              |
            |                              |
            +------------------------------+
Figure 6: Merging graphs from A and B

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


            +------------+      +------------+
            | IS-IS Link |      | OSPF Link  |
            +------------+      +------------+
                       |          |
                       v          v
                      +------------+
                      |  empty     |
                      +------------+
                            |
                +-----------+-------------+
                |           |             |
                v           v             v
      +-------------+ +------------+ +-------------+
      | Interface 1 | | Phys. Link | | Interface 2 |
      +-------------+ +------------+ +-------------+
Figure 7: Removing circular dependencies after merging graphs from A and B

3.2. Intent and Assurance Graph

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

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

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

  • Capturing the intent would start by detecting that the service instance is actually a tunnel between the two devices, and stating that this tunnel must be functional. This solution is minimally invasive as it does not require to modify nor know 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 SLO. For instance, the latency and bandwidth requirements for the tunnel, if present in the service model
  • Decomposing the service instance into subservices would result in the assurance graph depicted in Figure 2, for instance.

To be applied, SAIN requires a mechanism mapping a service instance to the configuration actually required on the devices for that service instance to run. While the Figure 1 makes a distinction between the SAIN orchestrator and a different component providing the service instance configuration, in practice those two components are mostly likely combined. The internals of the orchestrator are 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, a subservice also defines its assurance, that is the method for assuring that a subservice behaves correctly.

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

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

3.4. Building the Expression Graph from the Assurance Graph

From the assurance graph is derived a so-called global computation graph. First, each subservice instance is transformed into a set of subservice expressions that take metrics and constants as input (i.e., sources of the DAG) and produce the status of the subservice, based on some heuristics. For instance, the health of an interface is 0 (minimal score) with the symptom "interface admin-down" if the interface is disabled in the configuration. Then for each service instance, the service expressions are constructed by combining the subservice expressions of its dependencies. The way service expressions are combined depends on the dependency types (impacting or informational). Finally, the global computation graph is built by combining the service expressions. In other words, the global computation graph encodes all the operations needed to produce health statuses from the collected metrics.

The two types of dependencies for combining subservices are:

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

The set of dependency type presented here is not exhaustive. More specific dependency types can be defined by extending the YANG model. Adding these new dependency types requires defining the corresponding operation for combining statuses of subservices.

Subservices shall be not be dependent on the protocol used to retrieve the metrics. To justify this, let's consider the interface operational status. Depending on the device capabilities, this status can be collected by an industry-accepted YANG module (IETF, Openconfig), 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. 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 [I-D.ietf-opsawg-service-assurance-yang]; they specify objects for assuring network services based on their decomposition into so-called subservices, according to the SAIN architecture.

These modules are intended for the following use cases:

  • Assurance graph configuration:

    • Subservices: configure a set of subservices to assure, by specifying their types and parameters.
    • Dependencies: configure the dependencies between the subservices, along with their types.
  • Assurance telemetry: export the health status of the subservices, along with the observed symptoms.

Some examples of YANG instances can be found in Appendix A of [I-D.ietf-opsawg-service-assurance-yang].

3.6. 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 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.

3.7. 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 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. In the proposed architecture, the SAIN orchestrator is coupled to a Service orchestrator which defines the kinds of service 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, 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 and depends on the Service orchestrator and underlying network system. In other terms, 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. Exactly like a DHCP server/data plane/IPFIX export can be considered as subservices for a device, exactly like a 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 considered as a subservice for a service function chain [RFC7665]. The assurance graph is created to be flexible and open, regardless of the subservice types, locations, or domains.

The SAIN architecture is also flexible in terms of distributed graphs. As shown in Figure 1, the architecture comprises several agents. Each agent is responsible for handling a subgraph of the assurance graph. The collector is responsible for fetching the 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.

3.8. Timing

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

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.

3.9. 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 secured: a compromised SAIN agent may 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.

5. IANA Considerations

This document includes no request to IANA.

6. Contributors

7. References

7.1. Normative 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-06, , <https://www.ietf.org/archive/id/draft-ietf-opsawg-service-assurance-yang-06.txt>.

7.2. Informative References

[Piovesan2017]
Piovesan, A. and E. Griffor, "Reasoning About Safety and Security: The Logic of Assurance", .
[RFC2865]
Rigney, C., Willens, S., Rubens, A., and W. Simpson, "Remote Authentication Dial In User Service (RADIUS)", RFC 2865, DOI 10.17487/RFC2865, , <https://www.rfc-editor.org/info/rfc2865>.
[RFC5424]
Gerhards, R., "The Syslog Protocol", RFC 5424, DOI 10.17487/RFC5424, , <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, , <https://www.rfc-editor.org/info/rfc5905>.
[RFC6242]
Wasserman, M., "Using the NETCONF Protocol over Secure Shell (SSH)", RFC 6242, DOI 10.17487/RFC6242, , <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, , <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, , <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, , <https://www.rfc-editor.org/info/rfc7665>.
[RFC7950]
Bjorklund, M., Ed., "The YANG 1.1 Data Modeling Language", RFC 7950, DOI 10.17487/RFC7950, , <https://www.rfc-editor.org/info/rfc7950>.
[RFC8199]
Bogdanovic, D., Claise, B., and C. Moberg, "YANG Module Classification", RFC 8199, DOI 10.17487/RFC8199, , <https://www.rfc-editor.org/info/rfc8199>.
[RFC8309]
Wu, Q., Liu, W., and A. Farrel, "Service Models Explained", RFC 8309, DOI 10.17487/RFC8309, , <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, , <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, , <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, , <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, , <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, , <https://www.rfc-editor.org/info/rfc8969>.

Appendix A. Changes between revisions

v03 - v04

v00 - v01

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
Jean Quilbeuf
Huawei
Diego R. Lopez
Telefonica I+D
Don Ramon de la Cruz, 82
Madrid 28006
Spain
Dan Voyer
Bell Canada
Canada
Thangam Arumugam
Cisco Systems, Inc.
Milpitas (California),
United States of America