Internet DRAFT - draft-ietf-anima-reference-model
draft-ietf-anima-reference-model
ANIMA M. Behringer, Ed.
Internet-Draft
Intended status: Informational B. Carpenter
Expires: May 27, 2019 Univ. of Auckland
T. Eckert
Futurewei Technologies Inc.
L. Ciavaglia
Nokia
J. Nobre
University of Vale do Rio dos Sinos
November 23, 2018
A Reference Model for Autonomic Networking
draft-ietf-anima-reference-model-10
Abstract
This document describes a reference model for Autonomic Networking
for managed networks. It defines the behaviour 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
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This Internet-Draft will expire on May 27, 2019.
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This document is subject to BCP 78 and the IETF Trust's Legal
Provisions Relating to IETF Documents
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 3
2. The Network View . . . . . . . . . . . . . . . . . . . . . . 4
3. The Autonomic Network Element . . . . . . . . . . . . . . . . 5
3.1. Architecture . . . . . . . . . . . . . . . . . . . . . . 5
3.2. The Adjacency Table . . . . . . . . . . . . . . . . . . . 6
3.3. State Machine . . . . . . . . . . . . . . . . . . . . . . 8
3.3.1. State 1: Factory Default . . . . . . . . . . . . . . 8
3.3.2. State 2: Enrolled . . . . . . . . . . . . . . . . . . 9
3.3.3. State 3: In ACP . . . . . . . . . . . . . . . . . . . 9
4. The Autonomic Networking Infrastructure . . . . . . . . . . . 10
4.1. Naming . . . . . . . . . . . . . . . . . . . . . . . . . 10
4.2. Addressing . . . . . . . . . . . . . . . . . . . . . . . 10
4.3. Discovery . . . . . . . . . . . . . . . . . . . . . . . . 12
4.4. Signaling Between Autonomic Nodes . . . . . . . . . . . . 12
4.5. Routing . . . . . . . . . . . . . . . . . . . . . . . . . 13
4.6. The Autonomic Control Plane . . . . . . . . . . . . . . . 13
4.7. Information Distribution (*) . . . . . . . . . . . . . . 13
5. Security and Trust Infrastructure . . . . . . . . . . . . . . 14
5.1. Public Key Infrastructure . . . . . . . . . . . . . . . . 14
5.2. Domain Certificate . . . . . . . . . . . . . . . . . . . 14
5.3. The MASA . . . . . . . . . . . . . . . . . . . . . . . . 15
5.4. Sub-Domains (*) . . . . . . . . . . . . . . . . . . . . . 15
5.5. Cross-Domain Functionality (*) . . . . . . . . . . . . . 15
6. Autonomic Service Agents (ASA) . . . . . . . . . . . . . . . 15
6.1. General Description of an ASA . . . . . . . . . . . . . . 15
6.2. ASA Life-Cycle Management . . . . . . . . . . . . . . . . 17
6.3. Specific ASAs for the Autonomic Network Infrastructure . 18
6.3.1. The enrollment ASAs . . . . . . . . . . . . . . . . . 18
6.3.2. The ACP ASA . . . . . . . . . . . . . . . . . . . . . 19
6.3.3. The Information Distribution ASA (*) . . . . . . . . 19
7. Management and Programmability . . . . . . . . . . . . . . . 19
7.1. Managing a (Partially) Autonomic Network . . . . . . . . 19
7.2. Intent (*) . . . . . . . . . . . . . . . . . . . . . . . 20
7.3. Aggregated Reporting (*) . . . . . . . . . . . . . . . . 21
7.4. Feedback Loops to NOC (*) . . . . . . . . . . . . . . . . 21
7.5. Control Loops (*) . . . . . . . . . . . . . . . . . . . . 22
7.6. APIs (*) . . . . . . . . . . . . . . . . . . . . . . . . 22
7.7. Data Model (*) . . . . . . . . . . . . . . . . . . . . . 23
8. Coordination Between Autonomic Functions (*) . . . . . . . . 24
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8.1. The Coordination Problem (*) . . . . . . . . . . . . . . 24
8.2. A Coordination Functional Block (*) . . . . . . . . . . . 25
9. Security Considerations . . . . . . . . . . . . . . . . . . . 25
9.1. Protection Against Outsider Attacks . . . . . . . . . . . 26
9.2. Risk of Insider Attacks . . . . . . . . . . . . . . . . . 27
10. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 27
11. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . 28
12. Contributors . . . . . . . . . . . . . . . . . . . . . . . . 28
13. References . . . . . . . . . . . . . . . . . . . . . . . . . 28
13.1. Normative References . . . . . . . . . . . . . . . . . . 28
13.2. Informative References . . . . . . . . . . . . . . . . . 28
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 30
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, as well as
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
enrol 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 [I-D.ietf-anima-stable-connectivity] 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 phase of an
Autonomic Networking solution. 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
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in this document, but are not yet reflected in the implementation
specifications. They are marked with an (*).
2. The Network View
This section describes the various elements in a network with
autonomic functions, and how these entities work together, on a high
level. Subsequent sections explain the detailed inside view for each
of the 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" (ASA), 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. In a vertical view,
a node always implements the ANI, plus it may have one or several
Autonomic Service Agents. ASAs may be standalone, or use other ASAs
in a hierarchical way.
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The Autonomic Networking Infrastructure (ANI) therefore is the
foundation for autonomic functions.
3. The Autonomic Network Element
This section explains the general architecture of an Autonomic
Network Element (Section 3.1), how it tracks its surrounding
environment in an Adjacency Table (Section 3.2), and the state
machine which defines the behaviour of the network element
(Section 3.3), based on that adjacency table.
3.1. Architecture
This section describes an 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 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, and
the level of the Autonomic Networking Infrastructure, 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 synchronisation functions |
| - Distribution of Intent and other information |
| - Aggregated reporting and feedback loops |
| - Routing |
|------------------------------------------------------------|
| Basic Operating System Functions |
+------------------------------------------------------------+
Figure 2: Model of an autonomic node
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The Autonomic Networking Infrastructure (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 (upper
part of Figure 2). It contains addressing and naming of autonomic
nodes, discovery, negotiation and synchronisation functions,
distribution of information, reporting and feedback loops, as well as
routing inside the Autonomic Control Plane.
The Generalized Autonomic Control Plane (GACP) is the summary of all
interactions of the Autonomic Networking Infrastructure with other
nodes and services. A specific implementation of the GACP is
referred to here as the Autonomic Control Plane (ACP), and described
in [I-D.ietf-anima-autonomic-control-plane].
The use cases of "Autonomics" such as self-management, self-
optimisation, etc, are implemented as Autonomic Service Agents. They
use the services and data structures of the underlying Autonomic
Networking Infrastructure, which should be self-managing.
The "Basic Operating System Functions" include the "normal OS",
including the network stack, security functions, etc.
Full AN nodes have the full Autonomic Networking Infrastructure, with
the full functionality described in this document. At a later stage
ANIMA may define a scope for constrained nodes with a reduced ANI and
well-defined minimal functionality. They are currently out of scope.
3.2. The Adjacency Table
Autonomic Networking is based on direct interactions between devices
of a domain. The Autonomic Control Plane (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 information about adjacent autonomic
nodes, at a minimum: node-ID, IP address in data plane, IP address in
ACP, domain, 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 contains information about non-enrolled nodes, nodes
of the same and other domains. The adjacency table may contain
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information about the validity and trust level of the adjacent
autonomic nodes.
The adjacency table is fed by the following inputs:
o 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 nodes learns about all
autonomic nodes around itself. The related standards track
documents ([I-D.ietf-anima-grasp],
[I-D.ietf-anima-bootstrapping-keyinfra],
[I-D.ietf-anima-autonomic-control-plane]) describe in detail how
link local discovery is used.
o Vendor re-direct: 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) re-direct; this is
typically a routable address.
o 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 mechansims require some administrator intervention.
The key purpose is to by-pass a non-autonomic device or network.
As this pertains to new devices, it is covered in appendix A and B
of [I-D.ietf-anima-bootstrapping-keyinfra].
The adjacency table is defining the behaviour of an autonomic node:
o 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 re-direct via a vendor MASA, which will be entered into the
adjacency table (see second bullet above). See
[I-D.ietf-anima-bootstrapping-keyinfra] for details.
o If the adjacent node has the same domain, it will authenticate
that adjacent node and, if successful, establish the Autonomic
Control Plane (ACP). See
[I-D.ietf-anima-autonomic-control-plane].
o Once the node is part of the ACP of a domain, it will use GRASP
[I-D.ietf-anima-grasp] to find Registrar(s) of its domain and
potentially other services.
o 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
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assistant" ASA, and act as a join assistant for neighboring nodes
that need to be bootstrapped. See Section 6.3.1.2 for details.
o Other behaviours are possible, for example establishing the ACP
also with devices of a sub-domain, to other domains, etc. Those
will likely be controlled by Intent. They are outside scope for
the moment. Note that Intent is distributed through the ACP;
therefore, a node can only adapt Intent driven behaviour once it
has joined the ACP. At the moment, ANIMA 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 / synchronization via GRASP [I-D.ietf-anima-grasp] is
being 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 consist of Autonomic Service Agents (ASAs). They
run logically above the AN Infrastructure, 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 LDevID (see Section 5.2), in persistent storage, to be available
after a powercycle event. For device types that cannot store the
LDevID in persistent storage, a powercycle 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 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.
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Transitions:
o Bootstrap event: The device enrols 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
[I-D.ietf-anima-bootstrapping-keyinfra] Section 3 for details on
enrollment.
o Powercycle event: The device loses all state tables. It remains
in state: 1.
3.3.2. State 2: Enrolled
An autonomic node is in the state "enrolled" 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:
o Joining ACP: The device establishes an ACP channel to an adjacent
device. See [I-D.ietf-anima-autonomic-control-plane] for details.
Next state: 3.
o Factory reset: A factory reset removes all configuration and the
domain identity (LDevID) from the device. Next state: 1.
o Powercycle 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 (e.g., it
must now enable the join assistant ASA, to help other devices to join
the domain. Other conditions may apply to such interactions, for
example to serve as a join assistant, the device must first discover
a bootstrap Registrar.
Transitions:
o Leaving ACP: The device drops the last (or only) ACP channel to an
adjacent device. Next state: 2.
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o Factory reset: A factory reset removes all configuration and the
domain identity (LDevID) from the device. Next state: 1.
o Powercycle event: The device loses all state tables, but not its
domain identity (LDevID). Next state: 2.
4. The Autonomic Networking Infrastructure
The Autonomic Networking Infrastructure provides a layer of common
functionality across an Autonomic Network. It provides the
elementary functions and services, as well as extensions. An
Autonomic Function, comprising of Autonomic Service Agents 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
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 [I-D.ietf-anima-autonomic-control-plane]. The device name is then
composed of a Registrar ID (for example taking a 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, the fourth field is the
sequential number for the device.
4.2. Addressing
Autonomic Service Agents (ASAs) need to communicate with each other,
using the autonomic addressing of the Autonomic Networking
Infrastructure of the node they reside on. This section describes
the addressing approach of the Autonomic Networking Infrastructure,
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 [I-D.ietf-anima-prefix-management].
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Autonomic addressing is a function of the Autonomic Networking
Infrastructure (lower part of Figure 2), specifically the Autonomic
Control Plane. ASAs do not have their own addresses. They may use
either API calls, or the autonomic addressing scheme of the Autonomic
Networking Infrastructure.
An autonomic addressing scheme has the following requirements:
o 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.
o 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.
o Flexibility: The addressing scheme must be flexible enough for
nodes to be able to move around, for the network to grow, split
and merge.
o Robustness: It should be as hard as possible for an administrator
to negatively affect addressing (and thus connectivity) in the
autonomic context.
o Stability: The addressing scheme should be as stable as possible.
However, implementations need to be able to recover from
unexpected address changes.
o 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
in virtual entities. The infrastructure, including the addressing
scheme, should be able to support this architecture.
o Simplicity: To make engineering simpler, and to give the human
administrator an easy way to trouble-shoot autonomic functions.
o Scale: The proposed scheme should work in any network of any size.
o 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" ([I-D.ietf-anima-autonomic-control-plane]).
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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, and therefore it needs to discover peers and other resources
in the network. This section describes various discovery functions
in an autonomic network.
Discovering nodes and their properties and capabilities: 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 services such as AAA should also be
discovered and not configured. Service discovery is required for
such tasks. An autonomic network can either leverage existing
service discovery functions, or use a new approach, or a mixture.
Thus the discovery mechanism could either be fully integrated with
autonomic signaling (next section) or could use an independent
discovery mechanism such as DNS Service Discovery or Service Location
Protocol. This choice could be made independently for each Autonomic
Service Agent, 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 for discovery, described
in [I-D.ietf-anima-grasp].
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
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able to handle any sort of technical objective, including ones that
require complex data structures. The document "A Generic Autonomic
Signaling Protocol (GRASP)" [I-D.ietf-anima-grasp] describes more
detailed requirements for discovery, negotiation and synchronization
in an autonomic network. It also defines a protocol, GRASP, for this
purpose, including an integrated but optional discovery protocol.
GRASP is normally expected to run inside the Autonomic Control Plane
(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 later phases also with autonomic nodes outside their own
domain. Therefore, an Autonomic Control Plane 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
[I-D.ietf-anima-autonomic-control-plane].
4.6. The Autonomic Control Plane
The "Autonomic Control Plane" carries the control protocols in an
autonomic network. In the architecture described here, it is
implemented as an overlay network. The document "An Autonomic
Control Plane" ([I-D.ietf-anima-autonomic-control-plane]) describes
the implementation details suggested here. 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 "overlay" term in routing
context. See [I-D.ietf-anima-stable-connectivity] 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. For example, Intent is distributed across an
autonomic domain, as explained in [RFC7575].
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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
[I-D.liu-anima-grasp-distribution]. Intent and information
distribution are not part of phase 1 of ANIMA.
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 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 within the trust domain and fully trusted, or
outside the trust domain and fully untrusted.
The default method to automatically bring up a trust infrastructure
is defined in the document "Bootstrapping Key Infrastructures"
[I-D.ietf-anima-bootstrapping-keyinfra]. The ASAs required for this
enrollment process are described in Section 6.3. An autonomic node
must implement the enrollment and join assistant ASAs. The registrar
ASA may be implemented only on a sub-set 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
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certificate. [I-D.ietf-anima-bootstrapping-keyinfra] describes how a
new device receives a domain certificate, and the certificate format.
5.3. The 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
[I-D.ietf-anima-bootstrapping-keyinfra].
5.4. Sub-Domains (*)
By default, sub-domains are treated as different domains. This
implies no trust between a domain and its sub-domains, and no trust
between sub-domains 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, alternative trust models should be defined, for
example to allow full or limited trust between domain and sub-domain.
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 (ASA)
This section describes how autonomic services run on top of the
Autonomic Networking Infrastructure.
6.1. General Description of an ASA
An Autonomic Service Agent (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 the GRASP protocol [I-D.ietf-anima-grasp] or
otherwise. Of course it also interacts with the specific targets of
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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
[I-D.carpenter-anima-asa-guidelines].
Thus we can distinguish at least three classes of ASAs:
o Simple ASAs with a small footprint that could run anywhere.
o Complex, possibly multi-threaded ASAs that have a significant
resource requirement and will only run on selected nodes.
o 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 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
[I-D.ietf-anima-grasp-api].
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 including languages that support object
constructs as well as traditional variables and structures. The APIs
should be designed with these factors in mind.
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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 run-time error conditions must be caught, leading to suitable
minimally disruptive recovery actions, also considering a complete
restart of the ASA. 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, and 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 its interactions
with the ANI should follow the same operating principles, hence
comply to a generic life-cycle management model.
The ASA life-cycle provides standard processes to:
o install ASA: copy the ASA code onto the node and start it,
o deploy ASA: associate the ASA instance with a (some) managed
network device(s) (or network function),
o control ASA execution: when and how an ASA executes its control
loop.
The life-cyle 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:
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o 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
o Control of ASA control-loop during the operation: a 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 Network Infrastructure
The following functions provide essential, required functionality in
an 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 support the trust enrollment process
described in Section 5. For details see
[I-D.ietf-anima-bootstrapping-keyinfra].
6.3.1. The 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 join assitant 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 ASA
This ASA includes the function of an autonomic node that helps a non-
enrolled, adjacent devices to enroll into the domain. This ASA must
be installed on all nodes, although only one join assistant needs to
be active on a given LAN. See also
[I-D.ietf-anima-bootstrapping-keyinfra].
6.3.1.3. The Join Registrar ASA
This ASA includes the join registrar function in an autonomic
network. This ASA does not need to be installed on all nodes, but
only on nodes that implement the Join Registrar function.
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6.3.2. The ACP ASA
This ASA includes the ACP function in an 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
[I-D.ietf-anima-autonomic-control-plane].
6.3.3. The Information Distribution ASA (*)
This ASA is currently out of scope in ANIMA, and provided here only
as background information.
This ASA includes the information distribution function in an
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 [I-D.liu-anima-grasp-distribution] 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 co-exists with traditional management
methods in most networks. Thus, autonomic behavior will be defined
for individual functions in most environments. Examples for overlap
are:
o Autonomic functions can use traditional methods and protocols
(e.g., SNMP and NETCONF) to perform management tasks, inside and
outside the ACP;
o Autonomic functions can conflict with behavior enforced by the
same traditional methods and protocols;
o 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
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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, 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 over 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., PCIM) and in other SDOs (e.g., DMTF
and TMF ZOOM).
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 [I-D.du-anima-an-intent].
Intent as well as other types of information are distributed via
GRASP, see [I-D.liu-anima-grasp-distribution].
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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 which 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
should happen on a network-wide basis [RFC7575].
Multiple simultaneous events can occur in an 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 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 which is described in the autonomic Intent.
Reporting in an autonomic network may be at the same abstraction
level as Intent. In this context, the aggregated view of current
operational status of an autonomic network can be used to switch to
different management modes. Despite the fact that autonomic
management should minimize the need for user intervention, possibly
there are some events that need to be addressed by human
administrator actions.
7.4. Feedback Loops to NOC (*)
Feedback loops are required in an autonomic network to allow the
intervention of a human administrator or central control systems,
while maintaining a default behaviour. 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.
Uni-directional notifications to the 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 co-exist with autonomic
methods, but are not covered in this draft.
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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
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 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 is one that 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:
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o pre-conditions that must be satisfied before the method can start
execution
o post-conditions that must be satisfied when the method has
finished execution
o 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 are adapted from
[I-D.ietf-supa-generic-policy-data-model]:
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).
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.
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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. The 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 performance
to a given (intended) operational point.
Several interaction types may exist among autonomic functions, for
example:
o 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.
o Dependency: An autonomic function cannot work without another one
being present or accessible in the autonomic network.
o 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 in
three states:
o 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 on identified
conflicts.
o 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 functions and on a per resource basis,
including the actions performed and their relationships. This map
provides the basis to identify conflicts that will happen at run-
time, categorize them and plan for the appropriate coordination
strategies/mechanisms.
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o At run-time, 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.
8.2. A 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:
o A common description of autonomic functions, their attributes and
life-cycle.
o A common representation of information and knowledge (e.g.,
interaction maps).
o 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 vulnerabilies
to become an insider attacker.
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9.1. Protection Against Outsider Attacks
Here, we assume that all systems involved in an 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 (such as a secured MASA service).
Traditional security methods for both implementation and operation
are outside scope for 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:
o protected against modification.
o authenticated.
o protected against replay attacks.
o confidentiality protected (encrypted).
o and that the AN protocols are robust against packet drops and man-
in-the-middle attacks.
How these requirements are met is covered in the AN standards track
documents that define the methods used, specifically
[I-D.ietf-anima-bootstrapping-keyinfra], [I-D.ietf-anima-grasp], and
[I-D.ietf-anima-autonomic-control-plane].
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 [I-D.ietf-anima-grasp]:
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.
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9.2. Risk of Insider Attacks
An 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 or protocol has vulnerabilities or is not
securely operated, an outside attacker has the following generic ways
to take control of an autonomic network:
o 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.
o Subverting a device which 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.
o Exploiting potentially yet unknown protocol vulnerabilities in the
AN or other protocols. Also this is 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, 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 it is therefore recommended to
introduce a sub-domain 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 requests no action by IANA.
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11. Acknowledgements
Many people have provided feedback and input to this document: Sheng
Jiang, Roberta Maglione, Jonathan Hansford, Jason Coleman, Artur
Hecker. Useful reviews were made by Joel Halpern, Radia Perlman,
Tianran Zhou and Christian Hopps.
12. Contributors
Significant contributions to this document have been made by John
Strassner and Bing Liu from Huawei, and Pierre Peloso from Nokia.
13. References
13.1. Normative References
[I-D.ietf-anima-autonomic-control-plane]
Eckert, T., Behringer, M., and S. Bjarnason, "An Autonomic
Control Plane (ACP)", draft-ietf-anima-autonomic-control-
plane-18 (work in progress), August 2018.
[I-D.ietf-anima-bootstrapping-keyinfra]
Pritikin, M., Richardson, M., Behringer, M., Bjarnason,
S., and K. Watsen, "Bootstrapping Remote Secure Key
Infrastructures (BRSKI)", draft-ietf-anima-bootstrapping-
keyinfra-17 (work in progress), November 2018.
[I-D.ietf-anima-grasp]
Bormann, C., Carpenter, B., and B. Liu, "A Generic
Autonomic Signaling Protocol (GRASP)", draft-ietf-anima-
grasp-15 (work in progress), July 2017.
[IDevID] IEEE Standard, , "IEEE 802.1AR Secure Device Identifier",
December 2009, <http://standards.ieee.org/findstds/
standard/802.1AR-2009.html>.
13.2. Informative References
[I-D.carpenter-anima-asa-guidelines]
Carpenter, B., Ciavaglia, L., Jiang, S., and P. Pierre,
"Guidelines for Autonomic Service Agents", draft-
carpenter-anima-asa-guidelines-05 (work in progress), June
2018.
[I-D.du-anima-an-intent]
Du, Z., Jiang, S., Nobre, J., Ciavaglia, L., and M.
Behringer, "ANIMA Intent Policy and Format", draft-du-
anima-an-intent-05 (work in progress), February 2017.
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[I-D.ietf-anima-grasp-api]
Carpenter, B., Liu, B., Wang, W., and X. Gong, "Generic
Autonomic Signaling Protocol Application Program Interface
(GRASP API)", draft-ietf-anima-grasp-api-02 (work in
progress), June 2018.
[I-D.ietf-anima-prefix-management]
Jiang, S., Du, Z., and B. Carpenter, "Autonomic IPv6 Edge
Prefix Management in Large-scale Networks", draft-ietf-
anima-prefix-management-07 (work in progress), December
2017.
[I-D.ietf-anima-stable-connectivity]
Eckert, T. and M. Behringer, "Using Autonomic Control
Plane for Stable Connectivity of Network OAM", draft-ietf-
anima-stable-connectivity-10 (work in progress), February
2018.
[I-D.ietf-supa-generic-policy-data-model]
Halpern, J. and J. Strassner, "Generic Policy Data Model
for Simplified Use of Policy Abstractions (SUPA)", draft-
ietf-supa-generic-policy-data-model-04 (work in progress),
June 2017.
[I-D.liu-anima-grasp-distribution]
Liu, B., Jiang, S., Xiao, X., Hecker, A., and Z.
Despotovic, "Information Distribution in Autonomic
Networking", draft-liu-anima-grasp-distribution-09 (work
in progress), October 2018.
[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>.
Behringer, et al. Expires May 27, 2019 [Page 29]
Internet-Draft AN Reference Model November 2018
Authors' Addresses
Michael H. Behringer (editor)
Email: Michael.H.Behringer@gmail.com
Brian Carpenter
Department of Computer Science
University of Auckland
PB 92019
Auckland 1142
New Zealand
Email: brian.e.carpenter@gmail.com
Toerless Eckert
Futurewei Technologies Inc.
2330 Central Expy
Santa Clara 95050
USA
Email: tte@cs.fau.de
Laurent Ciavaglia
Nokia
Villarceaux
Nozay 91460
FR
Email: laurent.ciavaglia@nokia.com
Jeferson Campos Nobre
University of Vale do Rio dos Sinos
Av. Unisinos, 950
Sao Leopoldo 91501-970
Brazil
Email: jcnobre@unisinos.br
Behringer, et al. Expires May 27, 2019 [Page 30]
