Internet Engineering Task Force (IETF) M. Behringer, Ed.
Request for Comments: 8993
Category: Informational B. Carpenter
ISSN: 2070-1721 Univ. of Auckland
T. Eckert
Futurewei Technologies Inc. USA
L. Ciavaglia
Nokia
J. Nobre
University of Vale do Rio dos Sinos
April
UFRGS
May 2021
A Reference Model for Autonomic Networking
Abstract
This document describes a reference model for Autonomic Networking
for managed networks. It defines the behavior of an autonomic node,
how the various elements in an autonomic context work together, and
how autonomic services can use the infrastructure.
Status of This Memo
This document is not an Internet Standards Track specification; it is
published for informational purposes.
This document is a product of the Internet Engineering Task Force
(IETF). It represents the consensus of the IETF community. It has
received public review and has been approved for publication by the
Internet Engineering Steering Group (IESG). Not all documents
approved by the IESG are candidates for any level of Internet
Standard; see Section 2 of RFC 7841.
Information about the current status of this document, any errata,
and how to provide feedback on it may be obtained at
https://www.rfc-editor.org/info/rfc8993.
Copyright Notice
Copyright (c) 2021 IETF Trust and the persons identified as the
document authors. All rights reserved.
This document is subject to BCP 78 and the IETF Trust's Legal
Provisions Relating to IETF Documents
(https://trustee.ietf.org/license-info) in effect on the date of
publication of this document. Please review these documents
carefully, as they describe your rights and restrictions with respect
to this document. Code Components extracted from this document must
include Simplified BSD License text as described in Section 4.e of
the Trust Legal Provisions and are provided without warranty as
described in the Simplified BSD License.
Table of Contents
1. Introduction
2. Network View
3. Autonomic Network Element
3.1. Architecture
3.2. Adjacency Table
3.3. State Machine
3.3.1. State 1: Factory Default
3.3.2. State 2: Enrolled
3.3.3. State 3: In ACP
4. Autonomic Networking Infrastructure
4.1. Naming
4.2. Addressing
4.3. Discovery
4.4. Signaling between Autonomic Nodes
4.5. Routing
4.6. Autonomic Control Plane
4.7. Information Distribution (*)
5. Security and Trust Infrastructure
5.1. Public Key Infrastructure
5.2. Domain Certificate
5.3. MASA
5.4. Subdomains (*)
5.5. Cross-Domain Functionality (*)
6. Autonomic Service Agents (ASAs)
6.1. General Description of an ASA
6.2. ASA Life-Cycle Management
6.3. Specific ASAs for the Autonomic Networking Infrastructure
6.3.1. Enrollment ASAs
6.3.2. ACP ASA
6.3.3. Information Distribution ASA (*)
7. Management and Programmability
7.1. Managing a (Partially) Autonomic Network
7.2. Intent (*)
7.3. Aggregated Reporting (*)
7.4. Feedback Loops to NOC (*)
7.5. Control Loops (*)
7.6. APIs (*)
7.7. Data Model (*)
8. Coordination between Autonomic Functions (*)
8.1. Coordination Problem (*)
8.2. Coordination Functional Block (*)
9. Security Considerations
9.1. Protection against Outsider Attacks
9.2. Risk of Insider Attacks
10. IANA Considerations
11. References
11.1. Normative References
11.2. Informative References
Acknowledgements
Contributors
Authors' Addresses
1. Introduction
The document "Autonomic Networking: Definitions and Design Goals"
[RFC7575] explains the fundamental concepts behind Autonomic
Networking and defines the relevant terms in this space and a high-
level reference model. [RFC7576] provides a gap analysis between
traditional and autonomic approaches.
This document defines this reference model with more detail to allow
for functional and protocol specifications to be developed in an
architecturally consistent, non-overlapping manner.
As discussed in [RFC7575], the goal of this work is not to focus
exclusively on fully autonomic nodes or networks. In reality, most
networks will run with some autonomic functions, while the rest of
the network is traditionally managed. This reference model allows
for this hybrid approach.
For example, it is possible in an existing, non-autonomic network to
enroll devices in a traditional way to bring up a trust
infrastructure with certificates. This trust infrastructure could
then be used to automatically bring up an Autonomic Control Plane
(ACP) and run traditional network operations over the secure and
self-healing ACP. See [RFC8368] for a description of this use case.
The scope of this model is therefore limited to networks that are to
some extent managed by skilled human operators, loosely referred to
as "professionally managed" networks. Unmanaged networks raise
additional security and trust issues that this model does not cover.
This document describes a first, simple, implementable the first phase of an Autonomic Networking solution.
solution that is both simple and implementable. It is expected that
the experience from this phase will be used in defining updated and
extended specifications over time. Some topics are considered
architecturally in this document but are not yet reflected in the
implementation specifications. They are marked with an (*).
2. Network View
This section describes the various elements in a network with
autonomic functions and explains how these entities work together on
a high level. Subsequent sections explain the detailed inside view
for each of the autonomic network Autonomic Network elements, as well as the network
functions (or interfaces) between those elements.
Figure 1 shows the high-level view of an Autonomic Network. It
consists of a number of autonomic nodes, which interact directly with
each other. Those autonomic nodes provide a common set of
capabilities across the network, called the "Autonomic Networking
Infrastructure (ANI)". The ANI provides functions like naming,
addressing, negotiation, synchronization, discovery, and messaging.
Autonomic functions typically span several, possibly all, nodes in
the network. The atomic entities of an autonomic function are called
the "Autonomic Service Agents (ASAs)", which are instantiated on
nodes.
+- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +
: : Autonomic Function 1 : :
: ASA 1 : ASA 1 : ASA 1 : ASA 1 :
+- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +
: : :
: +- - - - - - - - - - - - - - + :
: : Autonomic Function 2 : :
: : ASA 2 : ASA 2 : :
: +- - - - - - - - - - - - - - + :
: : :
+- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +
: Autonomic Networking Infrastructure :
+- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +
+--------+ : +--------+ : +--------+ : +--------+
| Node 1 |--------| Node 2 |--------| Node 3 |----...-----| Node n |
+--------+ : +--------+ : +--------+ : +--------+
Figure 1: High-Level View of an Autonomic Network
In a horizontal view, autonomic functions span across the network, as
well as the Autonomic Networking Infrastructure. ANI. In a vertical view, a node always implements the
ANI, plus it may have one or several
Autonomic Service Agents. ASAs. ASAs may be standalone or
use other ASAs in a hierarchical way.
Therefore, the Autonomic Networking Infrastructure (ANI) ANI is the foundation for autonomic functions.
3. Autonomic Network Element
This section explains the general architecture of an autonomic
network Autonomic
Network element (Section 3.1), how it tracks its surrounding
environment in an adjacency table (Section 3.2), and the state
machine that defines the behavior of the network element
(Section 3.3), based on that adjacency table.
3.1. Architecture
This section describes an autonomic network Autonomic Network element and its internal
architecture. The reference model explained in the document
"Autonomic Networking: Definitions and Design Goals" [RFC7575] shows
the sources of information that an Autonomic Service Agent ASA can leverage: self-knowledge,
network knowledge (through discovery), Intent (see Section 7.2), and
feedback loops. There are two levels inside an autonomic node: the
level of Autonomic Service Agents ASAs and the level of the Autonomic Networking Infrastructure, ANI, with the former using the
services of the latter. Figure 2 illustrates this concept.
+------------------------------------------------------------+
| |
| +-----------+ +------------+ +------------+ |
| | Autonomic | | Autonomic | | Autonomic | |
| | Service | | Service | | Service | |
| | Agent 1 | | Agent 2 | | Agent 3 | |
| +-----------+ +------------+ +------------+ |
| ^ ^ ^ |
| - - | - - API level - -| - - - - - - - |- - - |
| V V V |
|------------------------------------------------------------|
| Autonomic Networking Infrastructure |
| - Data structures (ex: certificates, peer information) |
| - Generalized Autonomic Control Plane (GACP) |
| - Autonomic node addressing and naming |
| - Discovery, negotiation and synchronization functions |
| - Distribution of Intent and other information |
| - Aggregated reporting and feedback loops |
| - Routing |
|------------------------------------------------------------|
| Basic Operating System Functions |
+------------------------------------------------------------+
Figure 2: Model of an Autonomic Node
The Autonomic Networking Infrastructure ANI (lower part of Figure 2) contains node-specific data
structures (for example, trust information about itself and its
peers) as well as a generic set of functions, independent of a
particular usage. This infrastructure should be generic and support
a variety of Autonomic Service Agents ASAs (upper part of Figure 2). It contains addressing
and naming of autonomic nodes, discovery, negotiation and
synchronization functions, distribution of information, reporting,
feedback loops, and routing inside the Autonomic Control Plane. ACP.
The Generalized Autonomic Control Plane ACP (GACP) is the summary of all interactions of the Autonomic Networking Infrastructure
ANI with other nodes and services. A specific implementation of the
GACP is referred to here as the Autonomic Control Plane (ACP) ACP and described in [RFC8994].
The use cases of "Autonomics" (such as self-management, self-
optimization, etc.) are implemented as Autonomic Service Agents. ASAs. They use the services
and data structures of the underlying Autonomic
Networking Infrastructure, ANI, which should be self-managing. self-
managing.
The Basic Operating System Functions (lower part of Figure 2) include
the normal OS (e.g., the network stack and security functions).
Full Autonomic Networking Network (AN) nodes have the full Autonomic
Networking Infrastructure, ANI, with the full
functionality described in this document. At a later stage, the Autonomic Networking Integrated
Model and Approach (ANIMA)
ANIMA Working Group may define a scope for constrained nodes with a
reduced ANI and well-defined minimal functionality. These are
currently out of scope.
3.2. Adjacency Table
Autonomic Networking is based on direct interactions between devices
of a domain. The Autonomic Control Plane (ACP) ACP is normally constructed on a hop-by-hop basis.
Therefore, many interactions in the ANI are based on the ANI
adjacency table. There are interactions that provide input into the
adjacency table and other interactions that leverage the information
contained in it.
The ANI adjacency table contains, at a minimum, information about
adjacent autonomic nodes: node-ID, Node-ID, IP address in data plane, IP
address in ACP, domain, and certificate. An autonomic node maintains
this adjacency table up to date. The adjacency table only contains
information about other nodes that are capable of Autonomic
Networking; non-autonomic nodes are normally not tracked here.
However, the information is tracked independently of the status of
the peer nodes; specifically, it the adjacency table contains
information about non-
enrolled nodes, non-enrolled nodes of the same and other domains.
The adjacency table may contain information about the validity and
trust level of the adjacent autonomic nodes.
The adjacency table is fed by the following inputs:
* Link-local discovery: This interaction happens in the data plane,
using IPv6 link-local addressing only, because this addressing
type is itself autonomic. This way the node learns about all
autonomic nodes around itself. The related Standards Track
documents ([RFC8990], [RFC8995], and [RFC8994]) describe in detail
how link-local discovery is used.
* Vendor redirect: A new device may receive information on where its
home network is through a vendor-based Manufacturer Authorized
Signing Authority (MASA) (see Section 5.3) redirect; this is
typically a routable address.
* Non-autonomic input: A node may be configured manually with an
autonomic peer; it could learn about autonomic nodes through DHCP
options, DNS, and other non-autonomic mechanisms. Generally, such
non-autonomic mechanisms require some administrator intervention.
The key purpose is to bypass a non-autonomic device or network.
As this pertains to new devices, it is covered in Appendices A and
B of [RFC8995].
The adjacency table defines the behavior of an autonomic node:
* If the node has not bootstrapped into a domain (i.e., doesn't have
a domain certificate), it rotates through all nodes in the
adjacency table that claim to have a domain and will attempt
bootstrapping through them, one by one. One possible response is
a redirect via a vendor MASA, which will be entered into the
adjacency table (see second bullet above). See [RFC8995] for
details.
* If the adjacent node has the same domain, it will authenticate
that adjacent node and, if successful, establish the Autonomic
Control Plane (ACP). ACP. See
[RFC8994].
* Once the node is part of the ACP of a domain, it will use GRASP
[RFC8990] to find the registrar(s) of its domain and potentially
other services.
* If the node is part of an ACP and has discovered at least one
registrar in its domain via GRASP, it will start the join
assistant proxy
ASA and act as a join assistant proxy for neighboring nodes that need to be
bootstrapped. See Section 6.3.1.2 for details.
* Other behaviors are possible, for example, establishing the ACP
also
with devices of a subdomain, to subdomain or other domains, etc. domains. These will likely
be controlled by Intent and are outside scope for the
moment. scope of this
document. Note that Intent is distributed through the ACP;
therefore, a node can only adapt Intent-driven behavior once it
has joined the ACP. At the moment, the ANIMA Working Group does
not consider providing Intent outside the ACP; this can be
considered later.
Once a node has joined the ACP, it will also learn the ACP addresses
of its adjacent nodes and add them to the adjacency table to allow
for communication inside the ACP. Further autonomic domain
interactions will now happen inside the ACP. At this moment, only
negotiation and synchronization via GRASP [RFC8990] are defined.
(Note that GRASP runs in the data plane, as an input in building the
adjacency table, as well as inside the ACP.)
Autonomic Functions functions consist of Autonomic Service Agents (ASAs). ASAs. They run logically above the Autonomic Networking Infrastructure
ANI and may use the adjacency table, the ACP, negotiation and
synchronization through GRASP in the ACP, Intent, and other functions
of the ANI. Since the ANI only provides autonomic interactions
within a domain, autonomic functions can also use any other context
on a node, specifically the global data plane.
3.3. State Machine
Autonomic Networking applies during the full life cycle of a node.
This section describes a state machine of an autonomic node
throughout its life.
A device is normally expected to store its domain-specific identity,
the Local Device Identifier (LDevID) (see Section 5.2), in persistent
storage to be available after a power-cycle event. For device types
that cannot store the LDevID in persistent storage, a power-cycle
event is effectively equivalent to a factory reset.
3.3.1. State 1: Factory Default
An autonomic node leaves the factory in this state. In this state,
the node has no domain-specific configuration, specifically no
LDevID, and could be used in any particular target network. It does,
however, have a vendor/manufacturer-specific ID, the Initial Device
Identifier (IDevID) [IDevID]. Nodes without IDevID cannot be
autonomically and securely enrolled into a domain; they require
manual pre-staging, in which case the pre-staging takes them directly
to state 2.
Transitions:
* Bootstrap event: The device enrolls into a domain; as part of this
process it receives a domain identity (LDevID). If enrollment is
successful, the next state is state 2. See Section 3 of [RFC8995] for details
on enrollment.
* Power-cycle event: The device loses all state tables. It remains
in state 1.
3.3.2. State 2: Enrolled
An autonomic node is in the "enrolled" state if it has a domain
identity (LDevID) and has currently no ACP channel up. It may have
further configuration or state, for example, if it had been in state
3 before but lost all its ACP channels. The LDevID can only be
removed from a device through a factory reset, which also removes all
other state from the device. This ensures that a device has no stale
domain-specific state when entering the "enrolled" state from state
1.
Transitions:
* Joining ACP: The device establishes an ACP channel to an adjacent
device. See [RFC8994] for details. Next state: 3.
* Factory reset: A factory reset removes all configuration and the
domain identity (LDevID) from the device. Next state: 1.
* Power-cycle event: The device loses all state tables, but not its
domain identity (LDevID). It remains in state 2.
3.3.3. State 3: In ACP
In this state, the autonomic node has at least one ACP channel to
another device. The node can now participate in further autonomic
transactions, such as starting Autonomic Service Agents ASAs (e.g., it must now enable the
join assistant proxy ASA, to help other devices to join the domain. domain). Other
conditions may apply to such interactions, for example, to serve as a
join assistant, proxy, the device must first discover a bootstrap registrar.
Transitions:
* Leaving ACP: The device drops the last (or only) ACP channel to an
adjacent device. Next state: 2.
* Factory reset: A factory reset removes all configuration and the
domain identity (LDevID) from the device. Next state: 1.
* Power-cycle event: The device loses all state tables but not its
domain identity (LDevID). Next state: 2.
4. Autonomic Networking Infrastructure
The Autonomic Networking Infrastructure ANI provides a layer of common functionality across an Autonomic
Network. It provides the elementary functions and services, as well
as extensions. An
Autonomic Function, autonomic function, comprising of Autonomic Service Agents ASAs on nodes,
uses the functions described in this section.
4.1. Naming
Inside a domain, each autonomic device should be assigned a unique
name. The naming scheme should be consistent within a domain. Names
are typically assigned by a registrar at bootstrap time and are
persistent over the lifetime of the device. All registrars in a
domain must follow the same naming scheme.
In the absence of a domain-specific naming scheme, a default naming
scheme should use the same logic as the addressing scheme discussed
in [RFC8994]. The device name is then composed of a registrar ID Registrar-ID
(for example, taking a Media Access Control (MAC) address of the
registrar) and a device number. An example name would then look like
this:
0123-4567-89ab-0001
The first three fields are the MAC address, and the fourth field is
the sequential number for the device.
4.2. Addressing
Autonomic Service Agents (ASAs)
ASAs need to communicate with each other, using the autonomic
addressing of the Autonomic Networking
Infrastructure ANI of the node they reside on. This section
describes the addressing approach of the Autonomic Networking Infrastructure ANI used by ASAs.
Addressing approaches for the data plane of the network are outside
the scope of this document. These addressing approaches may be
configured and managed in the traditional way or negotiated as a
service of an ASA. One use case for such an autonomic function is
described in [RFC8992].
Autonomic addressing is a function of the Autonomic Networking
Infrastructure ANI (lower part of
Figure 2), specifically the Autonomic
Control Plane. ACP. ASAs do not have their own
addresses. They may use either API calls or the autonomic addressing
scheme of the Autonomic
Networking Infrastructure. ANI.
An autonomic addressing scheme has the following requirements:
* Zero-touch for simple networks: Simple networks should have
complete self-management of addressing and not require any central
address management, tools, or address planning.
* Low-touch for complex networks: If complex networks require
operator input for autonomic address management, it should be
limited to high-level guidance only, expressed in Intent.
* Flexibility: The addressing scheme must be flexible enough for
nodes to be able to move around and for the network to grow,
split, and merge.
* Robustness: It should be as hard as possible for an administrator
to negatively affect addressing (and thus connectivity) in the
autonomic context.
* Stability: The addressing scheme should be as stable as possible.
However, implementations need to be able to recover from
unexpected address changes.
* Support for virtualization: Autonomic functions can exist either
at the level of the physical network and physical devices or at
the level of virtual machines, containers, and networks. In
particular, autonomic nodes may support Autonomic Service Agents ASAs in virtual entities.
The infrastructure, including the addressing scheme, should be
able to support this architecture.
* Simplicity: To The addressing scheme should be simple to make
engineering simpler easier and to give the human administrator an easy way
to troubleshoot autonomic functions.
* Scale: The proposed scheme should work in any network of any size.
* Upgradability: The scheme must be able to support different
addressing concepts in the future.
The proposed addressing scheme is described in the document "An
Autonomic Control Plane (ACP)" [RFC8994].
4.3. Discovery
Traditionally, most of the information a node requires is provided
through configuration or northbound interfaces. An autonomic
function should rely on such northbound interfaces minimally or not
at all; therefore, it needs to discover peers and other resources in
the network. This section describes various discovery functions in
an autonomic network.
Discovering Autonomic Network.
First, discovering nodes and their properties and capabilities: A capabilities is a
core function to establish an autonomic domain is the mutual
discovery of autonomic nodes, primarily adjacent nodes and
secondarily off-link peers. This may, in principle, either leverage
existing discovery mechanisms or use new mechanisms tailored to the
autonomic context. An important point is that discovery must work in
a network with no predefined topology, ideally no manual
configuration of any kind, and with nodes starting up from factory
condition or after any form of failure or sudden topology change.
Discovering services: Network
Second, network services such as Authentication, Authorization, and
Accounting (AAA) should also be discovered and not configured.
Service discovery is required for such tasks. An
autonomic network Autonomic Network
can leverage existing service discovery functions, use a new
approach, or use a mixture.
Thus, the discovery mechanism could either be fully integrated with
autonomic signaling (next section) or use an independent discovery
mechanism such as DNS DNS-based Service Discovery or the Service Location
Protocol. This choice could be made independently for each Autonomic
Service Agent, ASA,
although the infrastructure might require some minimal lowest common
denominator (e.g., for discovering the security bootstrap mechanism
or the source of information distribution (Section 4.7)).
Phase 1 of Autonomic Networking uses GRASP [RFC8990] for discovery.
4.4. Signaling between Autonomic Nodes
Autonomic nodes must communicate with each other, for example, to
negotiate and/or synchronize technical objectives (i.e., network
parameters) of any kind and complexity. This requires some form of
signaling between autonomic nodes. Autonomic nodes implementing a
specific use case might choose their own signaling protocol, as long
as it fits the overall security model. However, in the general case,
any pair of autonomic nodes might need to communicate, so there needs
to be a generic protocol for this. A prerequisite for this is that
autonomic nodes can discover each other without any preconfiguration,
as mentioned above. To be generic, discovery and signaling must be
able to handle any sort of technical objective, including ones that
require complex data structures. The document "A GeneRic "GeneRic Autonomic
Signaling Protocol (GRASP)" [RFC8990] describes more detailed
requirements for discovery, negotiation, and synchronization in an
autonomic network.
Autonomic Network. It also defines a protocol, called GRASP, for
this purpose, including purpose; GRASP includes an integrated but optional discovery
protocol.
process.
GRASP is normally expected to run inside the ACP (see Section 4.6)
and to depend on the ACP for security. It may run insecurely for a
short time during bootstrapping.
An autonomic node will normally run a single instance of GRASP, used
by multiple ASAs. However, scenarios where multiple instances of
GRASP run in a single node, perhaps with different security
properties, are not excluded.
4.5. Routing
All autonomic nodes in a domain must be able to communicate with each
other, and in later phases, they must also be able to communicate
with autonomic nodes outside their own domain. Therefore, an
Autonomic Control Plane ACP
relies on a routing function. For Autonomic Networks to be
interoperable, they must all support one common routing protocol.
The routing protocol is defined in the ACP document [RFC8994].
4.6. Autonomic Control Plane
The Autonomic Control Plane ACP carries the control protocols in an
autonomic network. Autonomic Network. In
the architecture described in this document, it is implemented as an
overlay network. The document "An Autonomic Control Plane (ACP)"
[RFC8994] describes the implementation details suggested in this
document. This document uses the term "overlay" to mean a set of
point-to-point adjacencies congruent with the underlying
interconnection topology. The terminology may not be aligned with a
common usage of the term "overlay" in the routing context. See
[RFC8368] for uses cases for the ACP.
4.7. Information Distribution (*)
Certain forms of information require distribution across an autonomic
domain. The distribution of information runs inside the Autonomic
Control Plane. ACP. For
example, Intent is distributed across an autonomic domain, as
explained in [RFC7575].
Intent is the policy language of an Autonomic Network (see also
Section 7.2). It is a high-level policy and should change only
infrequently (order of days). Therefore, information such as Intent
should be simply flooded to all nodes in an autonomic domain, and
there is currently no perceived need to have more targeted
distribution methods. Intent is also expected to be monolithic and
flooded as a whole. One possible method for distributing Intent, as
well as other forms of data, is discussed in [GRASP-DISTRIB]. Intent
and information distribution are not part of phase 1 of ANIMA. the ANIMA Working Group
charter.
5. Security and Trust Infrastructure
An Autonomic Network is self-protecting. All protocols are secure by
default, without the requirement for the administrator to explicitly
configure security, with the exception of setting up a PKI
infrastructure.
Autonomic nodes have direct interactions between themselves, which
must be secured. Since an autonomic network Autonomic Network does not rely on
configuration, it is not an option to configure, for example, pre-
shared keys. A trust infrastructure such as a PKI infrastructure
must be in place. This section describes the principles of this
trust infrastructure. In this first phase of Autonomic Networking, a
device is either 1) within the trust domain and fully trusted or 2)
outside the trust domain and fully untrusted.
The default method to automatically bring up a trust infrastructure
is defined in the document "Bootstrapping Remote Secure Key
Infrastructure (BRSKI)" [RFC8995]. The ASAs required for this
enrollment process are described in Section 6.3. An autonomic node
must implement the enrollment and join assistant proxy ASAs. The registrar ASA
may be implemented only on a subset of nodes.
5.1. Public Key Infrastructure
An autonomic domain uses a PKI model. The root of trust is a
Certification Authority (CA). A registrar acts as a Registration
Authority (RA).
A minimum implementation of an autonomic domain contains one CA, one
registrar, and network elements.
5.2. Domain Certificate
Each device in an autonomic domain uses a domain certificate (LDevID)
to prove its identity. A new device uses its manufacturer-provided
certificate (IDevID) during bootstrap to obtain a domain certificate.
[RFC8995] describes how a new device receives a domain certificate
and defines the certificate format.
5.3. MASA
The Manufacturer Authorized Signing Authority (MASA) is a trusted
service for bootstrapping devices. The purpose of the MASA is to
provide ownership tracking of devices in a domain. The MASA provides
audit, authorization, and ownership tokens to the registrar during
the bootstrap process to assist in the authentication of devices
attempting to join an autonomic domain and to allow a joining device
to validate whether it is joining the correct domain. The details
for MASA service, security, and usage are defined in [RFC8995].
5.4. Subdomains (*)
By default, subdomains are treated as different domains. This
implies no trust between a domain and its subdomains and no trust
between subdomains of the same domain. Specifically, no ACP is
built, and Intent is valid only for the domain it is defined for
explicitly.
In phase 2 of ANIMA, the ANIMA Working Group charter, alternative trust models should
be defined, for example, to allow full or limited trust between
domain and subdomain.
5.5. Cross-Domain Functionality (*)
By default, different domains do not interoperate, no ACP is built,
and no trust is implied between them.
In the future, models can be established where other domains can be
trusted in full or for limited operations between the domains.
6. Autonomic Service Agents (ASAs)
This section describes how autonomic services run on top of the
Autonomic Networking Infrastructure. ANI.
6.1. General Description of an ASA
An Autonomic Service Agent (ASA) ASA is defined in [RFC7575] as "An agent implemented on an
autonomic node that implements an autonomic function, either in part
(in the case of a distributed function) or whole". Thus, it is a
process that makes use of the features provided by the ANI to achieve
its own goals, usually including interaction with other ASAs via
GRASP [RFC8990] or otherwise. Of course, it also interacts with the
specific targets of its function, using any suitable mechanism.
Unless its function is very simple, the ASA will need to handle
overlapping asynchronous operations. It may therefore be a quite
complex piece of software in its own right, forming part of the
application layer above the ANI. ASA design guidelines are available
in [ASA-GUIDELINES].
Thus, we can distinguish at least three classes of ASAs:
* Simple ASAs with a small footprint that could run anywhere.
* Complex, possibly multi-threaded ASAs that have a significant
resource requirement and will only run on selected nodes.
* A few 'infrastructure ASAs' that use basic ANI features in support
of the ANI itself, which must run in all autonomic nodes. These
are outlined in the following sections.
Autonomic nodes, and therefore their ASAs, know their own
capabilities and restrictions, derived from hardware, firmware, or
pre-installed software; they are "self-aware".
The role of an autonomic node depends on Intent and on the
surrounding network behaviors, which may include forwarding
behaviors, aggregation properties, topology location, bandwidth,
tunnel or translation properties, etc. For example, a node may
decide to act as a backup node for a neighbor, if its capabilities
allow it to do so.
Following an initial discovery phase, the node's properties and those
of its neighbors are the foundation of the behavior of a specific
node. A node and its ASAs have no pre-configuration for the
particular network in which they are installed.
Since all ASAs will interact with the ANI, they will depend on
appropriate application programming interfaces (APIs). It is
desirable that ASAs are portable between operating systems, so these
APIs need to be universal. An API for GRASP is described in
[RFC8991].
ASAs will, in general, be designed and coded by experts in a
particular technology and use case, not by experts in the ANI and its
components. Also, they may be coded in a variety of programming
languages, in particular, languages that support object constructs as
well as traditional variables and structures. The APIs should be
designed with these factors in mind.
It must be possible to run ASAs as non-privileged (user space)
processes except for those (such as the infrastructure ASAs) that
necessarily require kernel privilege. Also, it is highly desirable
that ASAs can be dynamically loaded on a running node.
Since autonomic systems must be self-repairing, it is of great
importance that ASAs are coded using robust programming techniques.
All runtime error conditions must be caught, leading to suitable
minimally disruptive recovery actions, also considering but a complete restart of the ASA.
ASA must also be considered. Conditions such as discovery failures
or negotiation failures must be treated as routine, with the ASA
retrying the failed operation, preferably with an exponential back-
off in the case of persistent errors. When multiple threads are
started within an ASA, these threads must be monitored for failures
and hangups, and appropriate action taken. Attention must be given
to garbage collection, so that ASAs never run out of resources.
There is assumed to be no human operator; again, in the worst case,
every ASA must be capable of restarting itself.
ASAs will automatically benefit from the security provided by the
ANI, specifically by the ACP and by GRASP. However, beyond that,
they are responsible for their own security, especially when
communicating with the specific targets of their function.
Therefore, the design of an ASA must include a security analysis
beyond 'use ANI security'.
6.2. ASA Life-Cycle Management
ASAs operating on a given ANI may come from different providers and
pursue different objectives. Management of ASAs and their
interactions with the ANI should follow the same operating principles
and thus comply to a generic life-cycle management model.
The ASA life cycle provides standard processes to:
* install ASA: copy the ASA code onto the node and start it.
* deploy ASA: associate the ASA instance with a (some) managed
network device(s) (or network function).
* control ASA execution: when and how an ASA executes its control
loop.
The life cycle will cover the sequential states below: Installation,
Deployment, Operation, and the transitional states in between.
This life cycle will also define which interactions ASAs have with
the ANI in between the different states. The noticeable interactions
are:
* Self-description of ASA instances at the end of deployment: Its
format needs to define the information required for the management
of ASAs by ANI entities.
* Control of the ASA control loop during the operation: A signaling Signaling
has to carry formatted messages to control ASA execution (at least
starting and stopping the control loop).
6.3. Specific ASAs for the Autonomic Networking Infrastructure
The following functions provide essential, required functionality in
an autonomic network Autonomic Network and are therefore mandatory to implement on
unconstrained autonomic nodes. They are described here as ASAs that
include the underlying infrastructure components, but implementation
details might vary.
The first three together (pledge, join proxy, join registrar) support together
the trust enrollment process described in Section 5. For details see
[RFC8995].
6.3.1. Enrollment ASAs
6.3.1.1. The Pledge ASA
This ASA includes the function of an autonomic node that bootstraps
into the domain with the help of an a join assistant proxy ASA (see below). Such
a node is known as a pledge during the enrollment process. This ASA
must be installed by default on all nodes that require an autonomic
zero-touch bootstrap.
6.3.1.2. The Join Assistant Proxy ASA
This ASA includes the function of an autonomic node that helps non-
enrolled, adjacent devices to enroll into the domain. This ASA must
be installed on all nodes, although only one join assistant proxy needs to be
active on a given LAN. See also [RFC8995].
6.3.1.3. The Join Registrar ASA
This ASA includes the Join Registrar function in an autonomic
network. Autonomic
Network. This ASA does not need to be installed on all nodes, but
only on nodes that implement the Join Registrar function.
6.3.2. ACP ASA
This ASA includes the ACP function in an autonomic network. Autonomic Network. In
particular, it acts to discover other potential ACP nodes and to
support the establishment and teardown of ACP channels. This ASA
must be installed on all nodes. For details, see Section 4.6 and
[RFC8994].
6.3.3. Information Distribution ASA (*)
This ASA is currently out of scope in the ANIMA Working Group charter
and is provided here only as background information.
This ASA includes the information distribution function in an
autonomic network.
Autonomic Network. In particular, it acts to announce the
availability of Intent and other information to all other autonomic
nodes. This ASA does not need to be installed on all nodes, but only
on nodes that implement the information distribution function. For
details, see Section 4.7.
Note that information distribution can be implemented as a function
in any ASA. See [GRASP-DISTRIB] for more details on how information
is suggested to be distributed.
7. Management and Programmability
This section describes how an Autonomic Network is managed and
programmed.
7.1. Managing a (Partially) Autonomic Network
Autonomic management usually coexists with traditional management
methods in most networks. Thus, autonomic behavior will be defined
for individual functions in most environments. Examples of overlap
are:
* Autonomic functions can use traditional methods and protocols
(e.g., SNMP and the Network Configuration Protocol (NETCONF)) to
perform management tasks, inside and outside the ACP.
* Autonomic functions can conflict with behavior enforced by the
same traditional methods and protocols.
* Traditional functions can use the ACP, for example, if
reachability on the data plane is not (yet) established.
The autonomic Intent is defined at a high level of abstraction.
However, since it is necessary to address individual managed
elements, autonomic management needs to communicate in lower-level
interactions (e.g., commands and requests). For example, it is
expected that the configuration of such elements be performed using
NETCONF and YANG modules as well as the monitoring be executed
through SNMP and MIBs.
Conflict can occur between autonomic default behavior, autonomic
Intent, and traditional management methods. Conflict resolution is
achieved in autonomic management through prioritization [RFC7575].
The rationale is that manual and node-based management have a higher
priority than autonomic management. Thus, the autonomic default
behavior has the lowest priority, then comes the autonomic Intent
(medium priority), and, finally, the highest priority is taken by
node-specific network management methods, such as the use of command-
line interfaces.
7.2. Intent (*)
Intent is not covered in the current implementation specifications.
This section discusses a topic for further research.
This section gives an overview of Intent and how it is managed.
Intent and Policy-Based Network Management (PBNM) is already
described inside the IETF (e.g., Policy Core Information Model
(PCIM)) and in other Standards Development Organizations (SDOs)
(e.g., the Distributed Management Task Force (DMTF) and TMF ZOOM). (DMTF)).
Intent can be described as an abstract, declarative, high-level
policy used to operate an autonomic domain, such as an enterprise
network [RFC7575]. Intent should be limited to high-level guidance
only; thus, it does not directly define a policy for every network
element separately.
Intent can be refined to lower-level policies using different
approaches. This is expected in order to adapt the Intent to the
capabilities of managed devices. Intent may contain role or function
information, which can be translated to specific nodes [RFC7575].
One of the possible refinements of the Intent is using Event-
Condition-Action (ECA) rules.
Different parameters may be configured for Intent. These parameters
are usually provided by the human operator. Some of these parameters
can influence the behavior of specific autonomic functions as well as
the way the Intent is used to manage the autonomic domain.
Intent is discussed in more detail in [ANIMA-INTENT]. Intent as well
as other types of information are distributed via GRASP; see
[GRASP-DISTRIB].
7.3. Aggregated Reporting (*)
Aggregated reporting is not covered in the current implementation
specifications. This section discusses a topic for further research.
An Autonomic Network should minimize the need for human intervention.
In terms of how the network should behave, this is done through an
autonomic Intent provided by the human administrator. In an
analogous manner, the reports that describe the operational status of
the network should aggregate the information produced in different
network elements in order to present the effectiveness of autonomic
Intent enforcement. Therefore, reporting in an autonomic network Autonomic Network
should happen on a network-wide basis [RFC7575].
Multiple simultaneous events can occur in an autonomic network Autonomic Network in the
same way they can happen in a traditional network. However, when
reporting to a human administrator, such events should be aggregated
to avoid notifications about individual managed elements. In this
context, algorithms may be used to determine what should be reported
(e.g., filtering) and in which way filtering), how it should be reported, and how different
events are related to each other. Besides that, an event in an
individual element can be compensated by changes in other elements to
maintain a network-wide target that is described in the autonomic
Intent.
Reporting in an autonomic network Autonomic Network may be at the same abstraction
level as Intent. In this context, the aggregated view of the current
operational status of an autonomic network Autonomic Network can be used to switch to
different management modes. Despite the fact that autonomic
management should minimize the need for user intervention, some
events may need to be addressed by the actions of a human
administrator.
7.4. Feedback Loops to NOC (*)
Feedback loops are required in an autonomic network Autonomic Network to allow the
intervention of a human administrator or central control systems
while maintaining a default behavior. Through a feedback loop, an
administrator must be prompted with a default action and has the
possibility to acknowledge or override the proposed default action.
Unidirectional notifications to the Network Operations Center (NOC)
that do not propose any default action and do not allow an override
as part of the transaction are considered like traditional
notification services, such as syslog. They are expected to coexist
with autonomic methods but are not covered in this document.
7.5. Control Loops (*)
Control loops are not covered in the current implementation
specifications. This section discusses a topic for further research.
Control loops are used in Autonomic Networking to provide a generic
mechanism to enable the autonomic system to adapt (on its own) to
various factors that can change the goals that the autonomic network Autonomic Network
is trying to achieve or how those goals are achieved. For example,
as user needs, business goals, and the ANI itself changes, self-
adaptation enables the ANI to change the services and resources it
makes available to adapt to these changes.
Control loops operate to continuously observe and collect data that
enables the autonomic management system to understand changes to the
behavior of the system being managed and then provide actions to move
the state of the system being managed toward a common goal. Self-
adaptive systems move decision making from static, pre-defined
commands to dynamic processes computed at runtime.
Most autonomic systems use a closed control loop with feedback. Such
control loops should be able to be dynamically changed at runtime to
adapt to changing user needs, business goals, and changes in the ANI.
7.6. APIs (*)
APIs
[RFC8991] defines a conceptual outline for an API for the GeneRic
Autonomic Signaling Protocol (GRASP). This conceptual API is
designed for ASAs to communicate with other ASAs through GRASP. Full
Standards Track API specifications are not covered in the current
implementation specifications.
This section discusses a topic for further research.
Most APIs are static, meaning that they are pre-defined and represent
an invariant mechanism for operating with data. An Autonomic Network
should be able to use dynamic APIs in addition to static APIs.
A dynamic API retrieves data using a generic mechanism and then
enables the client to navigate the retrieved data and operate on it.
Such APIs typically use introspection and/or reflection.
Introspection enables software to examine the type and properties of
an object at runtime, while reflection enables a program to
manipulate the attributes, methods, and/or metadata of an object.
APIs must be able to express and preserve the semantics of data
models. For example, software contracts [Meyer97] are based on the
principle that a software-intensive system, such as an Autonomic
Network, is a set of communicating components whose interaction is
based on precisely defined specifications of the mutual obligations
that interacting components must respect. This typically includes
specifying:
* pre-conditions that must be satisfied before the method can start
execution
* post-conditions that must be satisfied when the method has
finished execution
* invariant attributes that must not change during the execution of
the method
7.7. Data Model (*)
Data models are not covered in the current implementation
specifications. This section discusses a topic for further research.
The following definitions of "data model" and "information model" are
adapted from [SUPA-DATA].
An information model is a representation of concepts of interest to
an environment in a form that is independent of data repository, data
definition language, query language, implementation language, and
protocol. In contrast, a data model is a representation of concepts
of interest to an environment in a form that is dependent on data
repository, data definition language, query language, implementation
language, and protocol (typically, but not necessarily, all three). protocol.
The utility of an information model is to define objects and their
relationships in a technology-neutral manner. This forms a
consensual vocabulary that the ANI and ASAs can use. A data model is
then a technology-specific mapping of all or part of the information
model to be used by all or part of the system.
A system may have multiple data models. Operational Support Systems,
for example, typically have multiple types of repositories, such as
SQL and NoSQL, to take advantage of the different properties of each.
If multiple data models are required by an autonomic system, then an
information model should be used to ensure that the concepts of each
data model can be related to each other without technological bias.
A data model is essential for certain types of functions, such as a
Model-Reference Adaptive Control Loop (MRACL). More generally, a
data model can be used to define the objects, attributes, methods,
and relationships of a software system (e.g., the ANI, an autonomic
node, or an ASA). A data model can be used to help design an API, as
well as any language used to interface to the Autonomic Network.
8. Coordination between Autonomic Functions (*)
Coordination between autonomic functions is not covered in the
current implementation specifications. This section discusses a
topic for further research.
8.1. Coordination Problem (*)
Different autonomic functions may conflict in setting certain
parameters. For example, an energy efficiency function may want to
shut down a redundant link, while a load-balancing function would not
want that to happen. The administrator must be able to understand
and resolve such interactions to steer autonomic network Autonomic Network performance
to a given (intended) operational point.
Several interaction types may exist among autonomic functions, for
example:
* Cooperation: An autonomic function can improve the behavior or
performance of another autonomic function, such as a traffic
forecasting function used by a traffic allocation function.
* Dependency: An autonomic function cannot work without another one
being present or accessible in the autonomic network. Autonomic Network.
* Conflict: A metric value conflict is a conflict where one metric
is influenced by parameters of different autonomic functions. A
parameter value conflict is a conflict where one parameter is
modified by different autonomic functions.
Solving the coordination problem beyond one-by-one cases can rapidly
become intractable for large networks. Specifying a common
functional block on coordination is a first step to address the
problem in a systemic way. The coordination life cycle consists of
three states:
* At build-time, a "static interaction map" can be constructed on
the relationship of functions and attributes. This map can be
used to (pre-)define policies and priorities for identified
conflicts.
* At deploy-time, autonomic functions are not yet active/acting on
the network. A "dynamic interaction map" is created for each
instance of each autonomic function on a per-resource basis,
including the actions performed and their relationships. This map
provides the basis to identify conflicts that will happen at
runtime, categorize them, and plan for the appropriate
coordination strategies and mechanisms.
* At runtime, when conflicts happen, arbitration is driven by the
coordination strategies. Also, new dependencies can be observed
and inferred, resulting in an update of the dynamic interaction
map and adaptation of the coordination strategies and mechanisms.
Multiple coordination strategies and mechanisms exist and can be
devised. The set ranges from basic approaches (such as random
process or token-based process), to approaches based on time
separation and hierarchical optimization, to more complex approaches
(such as multi-objective optimization and other control theory
approaches and algorithms family). algorithm families).
8.2. Coordination Functional Block (*)
A common coordination functional block is a desirable component of
the ANIMA reference model. It provides a means to ensure network
properties and predictable performance or behavior, such as stability
and convergence, in the presence of several interacting autonomic
functions.
A common coordination function requires:
* A common description of autonomic functions, their attributes, and
life cycle.
* A common representation of information and knowledge (e.g.,
interaction maps).
* A common "control/command" interface between the coordination
"agent" and the autonomic functions.
Guidelines, recommendations, or BCPs can also be provided for aspects
pertaining to the coordination strategies and mechanisms.
9. Security Considerations
In this section, we distinguish outsider and insider attacks. In an
outsider attack, all network elements and protocols are securely
managed and operating, and an outside attacker can sniff packets in
transit, inject, and replay packets. In an insider attack, the
attacker has access to an autonomic node or other means (e.g., remote
code execution in the node by exploiting ACP-independent
vulnerabilities in the node platform) to produce arbitrary payloads
on the protected ACP channels.
If a system has vulnerabilities in the implementation or operation
(configuration), an outside attacker can exploit such vulnerabilities
to become an insider attacker.
9.1. Protection against Outsider Attacks
Here, we assume that all systems involved in an autonomic network Autonomic Network are
secured and operated according to best current practices. These
protection methods comprise traditional security implementation and
operation methods (such as code security, strong randomization
algorithms, strong passwords, etc.) as well as mechanisms specific to
an autonomic network Autonomic Network (such as a secured MASA service).
Traditional security methods for both implementation and operation
are outside the scope of this document.
AN-specific protocols and methods must also follow traditional
security methods, in that all packets that can be sniffed or injected
by an outside attacker are:
* protected against modification
* authenticated
* protected against replay attacks
* confidentiality protected (encrypted)
* and that
In addition, the AN protocols are should be robust against packet drops
and man-
in-the-middle attacks man-in-the-middle attacks.
How these requirements are met is covered in the AN Standards Track
documents that define the methods used, specifically [RFC8990],
[RFC8994], and [RFC8995].
Most AN messages run inside the cryptographically protected ACP. The
unprotected AN messages outside the ACP are limited to a simple
discovery method, defined in Section 2.5.2 of [RFC8990]: the
Discovery Unsolicited Link-Local (DULL) message, with detailed rules
on its usage.
If AN messages can be observed by a third party, they might reveal
valuable information about network configuration, security
precautions in use, individual users, and their traffic patterns. If
encrypted, AN messages might still reveal some information via
traffic analysis.
9.2. Risk of Insider Attacks
An autonomic network Autonomic Network consists of autonomic devices that form a
distributed self-managing system. Devices within a domain have
credentials issued from a common trust anchor and can use them to
create mutual trust. This means that any device inside a trust
domain can by default use all distributed functions in the entire
autonomic domain in a malicious way.
If an autonomic node
An inside attacker, or an outsider in the presence of protocol has
vulnerabilities or is not
securely operated, an outside attacker insecure operation, has the following generic ways
to take control of an autonomic network: Autonomic Network:
* Introducing a fake device into the trust domain by subverting the
authentication methods. This depends on the correct
specification, implementation, and operation of the AN protocols.
* Subverting a device that is already part of a trust domain and
modifying its behavior. This threat is not specific to the
solution discussed in this document and applies to all network
solutions.
* Exploiting potentially yet unknown protocol vulnerabilities in the
AN or other protocols. This is also a generic threat that applies
to all network solutions.
The above threats are, in principle, comparable to other solutions:
in the presence of design, implementation, or operational errors,
security is no longer guaranteed. However, the distributed nature of
AN, specifically the Autonomic Control Plane, ACP, increases the threat surface significantly.
For example, a compromised device may have full IP reachability to
all other devices inside the ACP and can use all AN methods and
protocols.
For the next phase of the ANIMA work, Working Group, it is therefore
recommended to introduce a subdomain security model to reduce the
attack surface and not expose a full domain to a potential intruder.
Furthermore, additional security mechanisms on the ASA level should
be considered for high-risk autonomic functions.
10. IANA Considerations
This document has no IANA actions.
11. References
11.1. Normative References
[IDevID] IEEE, "IEEE Standard for Local and metropolitan area
networks - Secure Device Identity",
DOI 10.1109/IEEESTD.2009.5367679, IEEE 802.1AR-2009,
December 2009,
<https://standards.ieee.org/standard/802_1AR-2009.html>. 802.1AR,
<https://1.ieee802.org/security/802-1ar>.
[RFC8990] Bormann, C., Carpenter, B., Ed., and B. Liu, Ed., "A
GeneRic "GeneRic
Autonomic Signaling Protocol (GRASP)", RFC 8990,
DOI 10.17487/RFC8990, April May 2021,
<https://www.rfc-editor.org/rfc/rfc8990>.
<https://www.rfc-editor.org/info/rfc8990>.
[RFC8994] Eckert, T., Ed., Behringer, M., Ed., and S. Bjarnason, "An
Autonomic Control Plane (ACP)", RFC 8994,
DOI 10.17487/RFC8994, April May 2021,
<https://www.rfc-editor.org/rfc/rfc8994>.
<https://www.rfc-editor.org/info/rfc8994>.
[RFC8995] Pritikin, M., Richardson, M., Eckert, T., Behringer, M.,
and K. Watsen, "Bootstrapping Remote Secure Key
Infrastructure (BRSKI)", RFC 8995, DOI 10.17487/RFC8995,
April
May 2021, <https://www.rfc-editor.org/rfc/rfc8995>. <https://www.rfc-editor.org/info/rfc8995>.
11.2. Informative References
[ANIMA-INTENT]
Du, Z., Jiang, S., Nobre, J., J. C., Ciavaglia, L., and M.
Behringer, "ANIMA Intent Policy and Format", Work in
Progress, Internet-Draft, draft-du-anima-an-intent-05, 14
February 2017,
<https://tools.ietf.org/html/draft-du-anima-an-intent-05>.
[ASA-GUIDELINES]
Carpenter, B., Ciavaglia, L., Jiang, S., and P. Peloso,
"Guidelines for Autonomic Service Agents", Work in
Progress, Internet-Draft, draft-ietf-anima-asa-guidelines-
00, 14 November 2020, <https://tools.ietf.org/html/draft-
ietf-anima-asa-guidelines-00>.
[GRASP-DISTRIB]
Liu, B., Ed., Xiao, X., Ed., Hecker, A., Jiang, S.,
Despotovic, Z., and B. Carpenter, "Information
Distribution over GRASP", Work in Progress, Internet-
Draft, draft-ietf-anima-grasp-distribution-02, 8 March
2021, <https://tools.ietf.org/html/draft-ietf-anima-grasp-
distribution-02>.
[Meyer97] Meyer, B., "Object-Oriented Software Construction (2nd
edition)", Prentice Hall, ISBN 978-0136291558, 1997.
[RFC7575] Behringer, M., Pritikin, M., Bjarnason, S., Clemm, A.,
Carpenter, B., Jiang, S., and L. Ciavaglia, "Autonomic
Networking: Definitions and Design Goals", RFC 7575,
DOI 10.17487/RFC7575, June 2015,
<https://www.rfc-editor.org/info/rfc7575>.
[RFC7576] Jiang, S., Carpenter, B., and M. Behringer, "General Gap
Analysis for Autonomic Networking", RFC 7576,
DOI 10.17487/RFC7576, June 2015,
<https://www.rfc-editor.org/info/rfc7576>.
[RFC8368] Eckert, T., Ed. and M. Behringer, "Using an Autonomic
Control Plane for Stable Connectivity of Network
Operations, Administration, and Maintenance (OAM)",
RFC 8368, DOI 10.17487/RFC8368, May 2018,
<https://www.rfc-editor.org/info/rfc8368>.
[RFC8991] Carpenter, B., Liu, B., Ed., Wang, W., and X. Gong,
"GeneRic Autonomic Signaling Protocol Application Program
Interface (GRASP API)", RFC 8991, DOI 10.17487/RFC8991,
April
May 2021, <https://www.rfc-editor.org/info/rfc8991>.
[RFC8992] Jiang, S., Ed., Du, Z., Carpenter, B., and Q. Sun,
"Autonomic IPv6 Edge Prefix Management in Large-Scale
Networks", RFC 8992, DOI 10.17487/RFC8992, April May 2021,
<https://www.rfc-editor.org/info/rfc8992>.
[SUPA-DATA]
Halpern, J. and J. Strassner, "Generic Policy Data Model
for Simplified Use of Policy Abstractions (SUPA)", Work in
Progress, Internet-Draft, draft-ietf-supa-generic-policy-
data-model-04, 18 June 2017, <https://tools.ietf.org/html/
draft-ietf-supa-generic-policy-data-model-04>.
Acknowledgements
The following people provided feedback and input to this document:
Sheng Jiang, Roberta Maglione, Jonathan Hansford, Jason Coleman, and
Artur Hecker. Useful reviews were made by Joel Halpern, Radia
Perlman, Tianran Zhou, and Christian Hopps.
Contributors
Significant contributions to this document were made by John
Strassner (Huawei), Bing Liu (Huawei), and Pierre Peloso (Nokia).
Authors' Addresses
Michael H. Behringer (editor)
Email: Michael.H.Behringer@gmail.com
Brian Carpenter
Department
School of Computer Science
University of Auckland
PB 92019
Auckland 1142
New Zealand
Email: brian.e.carpenter@gmail.com
Toerless Eckert
Futurewei Technologies Inc. USA
2330 Central Expy
Santa Clara, CA 95050
United States of America
Email: tte+ietf@cs.fau.de
Laurent Ciavaglia
Nokia
Villarceaux
91460 Nozay
France
Email: laurent.ciavaglia@nokia.com
Jéferson Campos Nobre
Federal University of Vale do Rio dos Sinos Grande do Sul (UFRGS)
Av. Unisinos, 950
São Leopoldo-RS
93022-750 Bento Gonçalves, 9500
Porto Alegre-RS
91501-970
Brazil
Email: jcnobre@inf.ufrgs.br