| OPSAWG | B. Claise |
| Internet-Draft | J. Quilbeuf |
| Intended status: Informational | Cisco Systems, Inc. |
| Expires: May 7, 2020 | November 4, 2019 |
Service Assurance for Intent-based Networking Architecture
draft-claise-opsawg-service-assurance-architecture-00
This document describes the architecture for Service Assurance for Intent-based Networking (SAIN). This architecture aims at assuring that service instances are correctly running. As services rely on multiple sub-services by the underlying network devices, getting the assurance of a healthy service is only possible with a holistic view of network devices. This architecture not only helps to correlate the service degradation with the network root cause but also the impacted services impacted when a network component fails or degrades.
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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.
Agent (SAIN Agent): Component that communicates with a device, a set of devices, or another agent to build an expression tree from a received assurance tree and perform the corresponding computation.
Assurance Tree: DAG representing the assurance case for one or several service instances. The nodes are the service instances themselves and the subservices, the edges indicate a dependency relations.
Collector (SAIN collector): Component that fetches the computer-consumable output of the agent(s) and displays it in a user friendly form or process it locally.
DAG: Directed Acyclic Graph.
ECMP: Equal Cost Multiple Paths
Expression Tree: Generic term for a DAG representing a computation in SAIN. More specific terms are:
Impacting Dependency: Type of dependency in the assurance tree. The status of the dependency is completely taken into account by the dependent service instance or subservice.
Informational Dependency: Type of dependency in the assurance tree. Only the symptoms (i.e. for informational reasons) are taken into account in the dependent service instance or subservice. In particular, the score is not taken into account.
Metric: Information retrieved from a network device.
Metric Engine: Maps metrics to a list of candidate metric implementations depending on the target model.
Metric Implementation: Actual way of retrieving a metric from a device.
Network Service YANG Module: describes the characteristics of service, as agreed upon with consumers of that service [RFC8199].
Service Instance: A specific instance of a service.
Orchestrator (SAIN Orchestrator): Component of SAIN in charge of fetching the configuration specific to each service instance and converting it into an assurance tree.
Health status: Score and symptoms indicating whether a service instance or a subservice is heathy. A non-maximal score MUST always be explained by one or more symptoms.
Subservice: Part of an assurance tree that assures a specific feature or subpart of the network system.
Symptom: Reason explaining why a service instance or a subservice is not completely healthy.
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.""
In other words, orchestrators deploy Network Service YANG Modules through the configuration of Network Element YANG Modules. Network configuration is based on those YANG data models, 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 correctly (for example 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. 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 elements [RFC7011], syslog plain text [RFC3164], TACACS+ [I-D.ietf-opsawg-tacacs], RADIUS [RFC2138], 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 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 service(s) is(are) impacted when this specific optic dBM begins to degrade? Which application is 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 “The service works correctly” and trying to enforce it. Such approaches are mainly suited for greenfield deployments.
Instead of approaching intent from a declarative way, this framework focuses on already defined services and tries to infer the meaning of “The service works correctly”. To do so, the framework works from an assurance tree, deduced from the service definition and from the network configuration. This assurance tree is decomposed into components, which are then assured independently. The root of the assurance tree represents the service to assure, and its children represent components identified as its direct dependencies; each component can have dependencies as well.
When a service is degraded, the framework will highlight where in the assurance service tree to look, as opposed to going hop by hop to troubleshoot the issue. Not only can can this framework help to correlate service degradation with network root cause/symptoms, but it can deduce from the assurance tree 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.
SAIN aims at assuring that service instances are correctly running. 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
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".
The overall architecture of our solution is presented in Figure 1. The assurance tree along some other configuration options is sent to the SAIN agents who are responsible for building the expression tree and computing the statuses in a distributed manner. The collector is in charge of collecting and displaying the current status of the assured service instances.
Network +-----------------+ +-------------------+
Service --------> | (SAIN) | | (SAIN) |
Instance | Orchestrator | | Collector |
Configuration +-----------------+ +-------------------+
| ^
| Configuration | health Status
| (assurance tree) | (score + symptoms)
V | streamed
+-------------------+ | via telemetry
|+-------------------+ |
||+-------------------+ |
+|| (SAIN) |---------+
+| agent |
+-------------------+
^ ^ ^
| | |
| | | Metric Collection
V V V
+-------------------------------------------------------------+
| Network |
| |
+-------------------------------------------------------------+
Figure 1: SAIN Architecture
In order to produce the score assigned to a service instance, the architecture performs the following tasks:
As said above, the goal of SAIN is to produce a health status for each service instance to assure, by collecting some metrics and applying operations on them. To meet that goal, the service is decomposed into an assurance tree formed by subservices linked through dependencies. Each subservice is then turned into expressions that are combined according to the dependencies between the subservices in order to obtain the expression tree which details how to fetch the metrics and how to compute the health status for each service instances. The expression tree is then implemented by the SAIN agents. The architecture also exports the health status of each subservice.
In order to structure the assurance of a service instance, the service instance is decomposed into so-called subservices. Each subservice focuses on a specific feature or subpart of the network system.
The decomposition into subservices is at the heart of this architecture, for the following reasons.
The assurance tree 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 tree 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 |
+-----------------+
|
+-------------------+-------------------+
| | |
+-------------+ +-------------+ +--------------+
| Peer1 | | Peer2 | | IP |
| Tunnel | | Tunnel | | Connectivity |
| Interface | | Interface | | |
+-------------+ +-------------+ +--------------}
| | |
+-------------+ +-------------+ +-------------+
| Peer1 | | Peer2 | | IS-IS |
| Physical | | Physical | | Routing |
| Interface | | Interface | | Protocol |
+-------------+ +-------------+ +-------------+
| |
+-------------+ +-------------+
| | | |
| Peer1 | | Peer2 |
| Device | | Device |
+-------------+ +-------------+
Figure 2: Assurance Tree Example
Depicting the assurance tree helps the operator to understand (and assert) the decomposition. The assurance tree 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 tree. As a first example, a change of routing protocol from IS-IS to OSPF would change the assurance tree 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.
The SAIN orchestrator analyzes the configuration of a service instance to:
The SAIN orchestrator must be able to analyze configuration from various devices and produce the assurance tree.
To schematize what a SAIN orchestrator does, assume that the configuration for a service instance touches 2 devices and configure on each device a virtual tunnel interface. Then:
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 standardization.
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.
A subservice is characterized by a list of metrics to fetch and a list of computations to apply to these metrics in order to produce a health status. Subservices, as services, have high-level parameters which defines which object should be assured.
From the assurance tree is derived a so-called expression tree, which is actually a DAG whose sources are constants or metrics and other nodes are operators. The expression tree encodes all the operations needed to produce heath statuses from the collected metrics.
Subservices shall be device independent. To justify this, let's consider the interface operational status. Dependending 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.
In order to keep subservices independent from metric collection method, or, expressed differently, to support multiple combinations of platforms, OSes, and even vendors, the framework 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.
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.
The interfaces between the architecture components are open thanks to YANG module(I-D.claise-opsawg-service-assurance-yang) defines 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:
TO BE COMPLETED
This document includes no request to IANA.
| [RFC2119] | Bradner, S., "Key words for use in RFCs to Indicate Requirement Levels", BCP 14, RFC 2119, DOI 10.17487/RFC2119, March 1997. |
| [RFC8174] | Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC 2119 Key Words", BCP 14, RFC 8174, DOI 10.17487/RFC8174, May 2017. |
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