ANIMA M. Behringer, Ed.
Internet-Draft Cisco Systems
Intended status: Informational B. Carpenter
Expires: January 1, 2016 Univ. of Auckland
T. Eckert
Cisco
L. Ciavaglia
Alcatel Lucent
B. Liu
Huawei Technologies
J. Nobre
Federal University of Rio Grande do Sul
J. Strassner
Huawei Technologies
June 30, 2015
A Reference Model for Autonomic Networking
draft-behringer-anima-reference-model-03
Abstract
This document describes a reference model for Autonomic Networking.
The goal is to define how the various elements in an autonomic
context work together, to describe their interfaces and relations.
While the document is written as generally as possible, the initial
solutions are limited to the chartered scope of the WG.
Status of This Memo
This Internet-Draft is submitted in full conformance with the
provisions of BCP 78 and BCP 79.
Internet-Drafts are working documents of the Internet Engineering
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Internet-Drafts are draft documents valid for a maximum of six months
and may be updated, replaced, or obsoleted by other documents at any
time. It is inappropriate to use Internet-Drafts as reference
material or to cite them other than as "work in progress."
This Internet-Draft will expire on January 1, 2016.
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Copyright Notice
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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. Full AN Nodes . . . . . . . . . . . . . . . . . . . . . . 6
3.3. Constrained AN Nodes (*) . . . . . . . . . . . . . . . . 6
4. The Autonomic Networking Infrastructure . . . . . . . . . . . 6
4.1. Naming . . . . . . . . . . . . . . . . . . . . . . . . . 6
4.1.1. Naming requirements . . . . . . . . . . . . . . . . . 6
4.1.2. Proposed Mechanisms . . . . . . . . . . . . . . . . . 7
4.2. Addressing . . . . . . . . . . . . . . . . . . . . . . . 8
4.2.1. Requirements and Fundamental Concepts . . . . . . . . 9
4.2.2. The Base Addressing Scheme . . . . . . . . . . . . . 10
4.2.3. Possible Sub-Schemes . . . . . . . . . . . . . . . . 11
4.2.4. Address Hierarchy . . . . . . . . . . . . . . . . . . 12
4.3. Discovery . . . . . . . . . . . . . . . . . . . . . . . . 13
4.4. Signaling Between Autonomic Nodes . . . . . . . . . . . . 13
4.5. Intent Distribution . . . . . . . . . . . . . . . . . . . 14
4.6. Routing . . . . . . . . . . . . . . . . . . . . . . . . . 14
4.7. The Autonomic Control Plane . . . . . . . . . . . . . . . 14
5. Security and Trust Infrastructure . . . . . . . . . . . . . . 15
5.1. Public Key Infrastructure . . . . . . . . . . . . . . . . 15
5.2. Domain Certificate . . . . . . . . . . . . . . . . . . . 15
5.3. The MASA . . . . . . . . . . . . . . . . . . . . . . . . 15
5.4. Sub-Domains (*) . . . . . . . . . . . . . . . . . . . . . 15
5.5. Cross-Domain Functionality (*) . . . . . . . . . . . . . 15
6. Autonomic Service Agents (ASA) . . . . . . . . . . . . . . . 16
6.1. General Description of an ASA . . . . . . . . . . . . . . 16
6.2. Specific ASAs for the Enrolment Process . . . . . . . . . 16
6.2.1. The Enrolment ASA . . . . . . . . . . . . . . . . . . 16
6.2.2. The Enrolment Proxy ASA . . . . . . . . . . . . . . . 16
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6.2.3. The Registrar ASA . . . . . . . . . . . . . . . . . . 16
7. Management and Programmability . . . . . . . . . . . . . . . 16
7.1. How an AN Network Is Managed . . . . . . . . . . . . . . 16
7.2. Intent (*) . . . . . . . . . . . . . . . . . . . . . . . 17
7.3. Aggregated Reporting (*) . . . . . . . . . . . . . . . . 18
7.4. Feedback Loops to NOC(*) . . . . . . . . . . . . . . . . 19
7.5. Control Loops (*) . . . . . . . . . . . . . . . . . . . . 19
7.5.1. Types of Control (*) . . . . . . . . . . . . . . . . 20
7.5.2. Types of Control Loops (*) . . . . . . . . . . . . . 20
7.5.3. Management of an Autonomic Control Loop (*) . . . . . 21
7.5.4. Elements of an Autonomic Control Loop (*) . . . . . . 22
7.6. APIs (*) . . . . . . . . . . . . . . . . . . . . . . . . 22
7.6.1. Dynamic APIs (*) . . . . . . . . . . . . . . . . . . 22
7.6.2. APIs and Semantics(*) . . . . . . . . . . . . . . . . 23
7.6.3. API Considerations (*) . . . . . . . . . . . . . . . 23
7.7. Data Model (*) . . . . . . . . . . . . . . . . . . . . . 23
8. Coordination Between Autonomic Functions (*) . . . . . . . . 24
8.1. The Coordination Problem (*) . . . . . . . . . . . . . . 24
8.2. A Coordination Functional Block (*) . . . . . . . . . . . 25
9. Security Considerations . . . . . . . . . . . . . . . . . . . 26
9.1. Threat Analysis . . . . . . . . . . . . . . . . . . . . . 26
10. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 27
11. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . 27
12. References . . . . . . . . . . . . . . . . . . . . . . . . . 27
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 28
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. In section
5 it describes a high level reference model. 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. While the document is written as
generally as possible, the initial solutions are limited to the
chartered scope of the WG.
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.
This is a living document and will evolve with the technical
solutions developed in the ANIMA WG. Sections marked with (*) do not
represent current charter items. While this document must give a
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long term architectural view, not all functions will be standardized
at the same time.
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.
The Autonomic Networking Infrastructure (ANI) therefore is the
foundation for autonomic functions. The current charter of the ANIMA
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WG is to specify the ANI, using a few autonomic functions as use
cases.
3. The Autonomic Network Element
3.1. Architecture
This section describes an autonomic network element and its internal
architecture. The reference model explained in
[I-D.irtf-nmrg-autonomic-network-definitions] shows the sources of
information that an autonomic service agent can leverage: Self-
knowledge, network knowledge (through discovery), Intent, and
feedback loops. Fundamentally, 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) |
| - Autonomic Control Plane |
| - discovery, negotiation and synchronisation functions |
| - Intent distribution |
| - aggregated reporting and feedback loops |
| - routing |
|------------------------------------------------------------|
| Basic Operating System Functions |
+------------------------------------------------------------+
Figure 2: Model of an autonomic node
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). The Autonomic Control Plane is the summary of all
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interactions of the Autonomic Networking Infrastructure with other
nodes and services.
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. The underlying Autonomic Networking
Infrastructure should itself be self-managing.
The "Basic Operating System Functions" include the "normal OS",
including the network stack, security functions, etc.
3.2. Full AN Nodes
Full AN nodes have the full Autonomic Networking Infrastructure, with
the full functionality (details to be worked out). They support all
the capabilities outlined in the rest of the document. [tbc]
3.3. Constrained AN Nodes (*)
Constrained nodes have a reduced ANI, with a well-defined minimal
functionality (details to be worked out): They do need to be able to
join the network, and communicate with at least a helper node which
has full ANI functionality. Capabilities of constrained nodes need
to be defined here. [tbc]
4. The Autonomic Networking Infrastructure
The Autonomic Networking Infrastructure provides a layer of common
functionality across an Autonomic Network. It comprises "must
implement" functions and services, as well as extensions.
An Autonomic Function, comprising of Autonomic Service Agents on
nodes, can rely on the fact that all nodes in the network implement
at least the "must implement" functions.
4.1. Naming
4.1.1. Naming requirements
o Representing each device
Inside a domain, each autonomic device needs a domain specific
identifier.
[Open Questions] Are there devices that don't need names? Do
ASAs need names?
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o Uniqueness
The names MUST NOT collide within one autonomic domain.
It is acceptable that the names in different domains collide,
since they could be distinguished by domains.
o Semantic Encoding
It is RECOMMENDED that the names encode some semantics rather
than meaningless strings. The semantics might be:
+ Location
+ Device type
+ Functional role
+ Ownership
+ etc.
This is for ease of management consideration that network
administrators could easily recognize the device directly
through the names.
o Consistency
The devices' naming SHOULD follow the same pattern within a
domain.
4.1.2. Proposed Mechanisms
__
o Structured Naming Pattern
The whole name string could be divided into several fields,
each of which representing a specific semantic as described
above. For example: Location-DeviceType-FunctionalRole-
DistinguisherNumber@NameofDomain.
The structure should be flexible that some fields are optional.
When these optional fields are added, the name could still be
recognized as the previous one. In above example, the
"DistinguisherNumber" and "NameofDomain" are mandatory whereas
others are optional. At initial stage, the devices might be
only capable of self-generating the mandatory fields and the
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"DeviceType" because of the lack of knowledge. Later, they
might have learned the "Location" and "FunctionalRole" and
added the fields into current name. However, the other devices
could still recognize it according to the same
"DistinguisherNumber".
o Advertised Common Fields
Some fields in the structured name might be common among the
domain (e.g. "Location" "NameofDomain"). Thus, these part of
the names could be advertised through Intent
DistributionSection 4.5.
o Self-generated Fields
The mandatory fields SHOULD be self-generated so that one
device could name itself sufficiently without any advertised
knowledges.
There should various methods for a device to extract/generate a
proper word for each mandatory semantic fields (e.g.
"DeviceType", "DistinguisherNum") from its self-knowledge.
Detailed design of specific naming patterns and methods are out of
scope of this document.
4.2. Addressing
Autonomic Service Agents (ASAs) need to communicate with each other,
using the autonomic addressing of the node they reside on. This
section describes the addressing approach of the Autonomic Networking
Infrastructure, used by ASAs. It does NOT describe addressing
approaches for the data plane of the network, which 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.jiang-auto-addr-management]. The addressing of the Autonomic
Networking Infrastructure is in scope for this section, the address
space they negotiate for the data plane is not.
Autonomic addressing is a function of the Autonomic Networking
Infrastructure (lower part of Figure 2). ASAs do not have their own
addresses. They may use either API calls, or the autonomic
addressing scheme of the Autonomic Networking Infrastructure.
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4.2.1. Requirements and Fundamental Concepts
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 Support for virtualization: Autonomic Nodes may support Autonomic
Service Agents in different virtual machines or containers. The
addressing scheme should 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.
These are the fundamental concepts of autonomic addressing:
o IPv6 only: Autonomic processes SHOULD (as defined in [RFC2119])
use exclusively IPv6, for simplicity reasons.
o Usage: Autonomic addresses are exclusively used for self-
management functions inside a trusted domain. They are not used
for user traffic. Communications with entities outside the
trusted domain use another address space, for example normally
managed routable address space.
o Separation: Autonomic address space is used separately from user
address space and other address realms. This supports the
robustness requirement. Link-local is considered not part of user
address space for this purpose.
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o Overlay network: Routeable addresses for AN nodes are used
exclusively in a secure overlay network which is the basis of the
ACP. This means that these addresses will be assigned to the
loopback interface in most operating systems. All other
interfaces exclusively use IPv6 link local for autonomic
functions. The usage of IPv6 link local addressing is discussed
in [RFC7404].
o Use-ULA: For these overlay addresses of autonomic nodes, we use
Unique Local Addresses (ULA), as specified in [RFC4193]. An
alternative scheme was discussed, using assigned ULA addressing.
The consensus was to use standard ULA, because it was deemed to be
sufficient.
o No external connectivity: They do not provide access to the
Internet. If a node requires further reaching connectivity, it
should use another, traditionally managed address scheme in
parallel.
4.2.2. The Base Addressing Scheme
The Base ULA addressing scheme for autonomic nodes has the following
format:
8 40 3 77
+--+--------------+------+------------------------------------------+
|FD| hash(domain) | Type | (sub-scheme) |
+--+--------------+------+------------------------------------------+
Figure 3: Base Addressing Scheme
The first 48 bits follow the ULA scheme, as defined in [RFC4193], to
which a type field is added:
o "FD" identifies a locally defined ULA address.
o The "global ID" is set here to be a hash of the domain name, which
results in a pseudo-random 40 bit value. It is calculated as the
first 40 bits of the MD5 hash of the domain name, in the example
"example.com".
o Type: Set to 000 (3 zero bits). This field allows different
address sub-schemes in the future. The goal is to start with a
minimal number of sub-scheme initially, but to allow for
extensions later if and when required. This addresses the
"upgradability" requirement. Assignment of types for this field
should be maintained by IANA.
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4.2.3. Possible Sub-Schemes
The sub-schemes listed here are not intended to be all supported
initially, but are listed for discussion. The final document should
define ideally only a single sub-scheme for now, and leave the other
"types" for later assignment.
4.2.3.1. Sub-Scheme 1
51 13 64
+------------------------+---------+--------------------------------+
| (base scheme) | Zone ID | Device ID |
+------------------------+---------+--------------------------------+
Figure 4: Addressing Scheme 1
The fields are defined as follows: [Editor's note: The lengths of the
fields is for discussion.]
o Zone ID: If set to all zero bits: Flat addressing scheme. Any
other value indicates a zone. See section Section 4.2.4 on how
this field is used in detail.
o Device ID: A unique value for each device, typically assigned by a
registrar.
The device ID is derived as follows: In an Autonomic Network, a
registrar is enrolling new devices. As part of the enrolment process
the registrar assigns a number to the device, which is unique for
this registrar, but not necessarily unique in the domain. The 64 bit
device ID is then composed as:
o 48 bit: Registrar ID, a number unique inside the domain that
identifies the registrar which assigned the name to the device. A
MAC address of the registrar can be used for this purpose.
o 16 bit: Device ID, a number which is unique for a given registrar,
to identify the device. This can be a sequentially assigned
number.
The "device ID" itself is unique in a domain (i.e., the Zone-ID is
not required for uniqueness). Therefore, a device can be addressed
either as part of a flat hierarchy (zone ID = 0), or with an
aggregation scheme (any other zone ID). An address with zone-ID 0
(zero) could be interpreted as an identifier, with another zone-ID as
a locator.
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4.2.3.2. Sub-Scheme 2
51 13 64-V ?
+------------------------+---------+----------------------------+---+
| (base scheme) | Zone ID | Device ID | V |
+------------------------+---------+----------------------------+---+
Figure 5: Addressing Scheme 2
The fields are defined as follows: [Editor's note: The lengths of the
fields is for discussion.]
o Zone ID: As in sub-scheme 1.
o Device ID: As in sub-scheme 1.
o V: Virtualization bit(s): 1 or more bits that indicate a virtual
context on an autonomic node.
In addition the scheme 1 (Section 4.2.3.1), this scheme allows the
direct addressing of specific virtual containers / VMs on an
autonomic node. An increasing number of hardware platforms have a
distributed architecture, with a base OS for the node itself, and the
support for hardware blades with potentially different OSs. The VMs
on the blades could be considered as separate autonomic nodes, in
which case it would make sense to be able to address them directly.
Autonomic Service Agents (ASAs) could be instantiated in either the
base OS, or one of the VMs on a blade. This addressing scheme allows
for the easy separation of the hardware context.
The location of the V bit(s) at the end of the address allows to
announce a single prefix for each autonomic node, while having
separate virtual contexts addressable directly.
4.2.4. Address Hierarchy
The "zone ID" allows for the definition of a simple address
hierarchy. If set to zero, the address scheme is flat. In this
case, the addresses primarily act as identifiers for the nodes. Used
like this, aggregation is not possible.
If aggregation is required, the 13 bit value allows for up to 8191
zones. (Theoretically, the 13 bits for the zone ID would allow also
for two levels of zones, introducing a sub-hierarchy. We do not
think this is required at this point, but a new type could be used in
the future to support such a scheme.)
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Another way to introduce hierarchy is to use sub-domains in the
naming scheme. The node names "node17.subdomainA.example.com" and
"node4.subdomainB.example.com" would automatically lead to different
ULA prefixes, which can be used to introduce a routing hierarchy in
the network, assuming that the subdomains are aligned with routing
areas.
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 intent distribution,
Section 4.5).
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,
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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 Discovery
and Negotiation Protocol for Autonomic Networking"
[I-D.carpenter-anima-gdn-protocol] describes more detailed
requirements for discovery, negotiation and synchronization in an
autonomic network. It also defines a protocol, GDNP, for this
purpose, including an integrated but optional discovery protocol.
4.5. Intent Distribution
Intent is the policy language of an Autonomic Network; see
Section 7.2 for general information on Intent. The distribution of
Intent is also a function of the Autonomic Control Plane. It is
expected that Intent will be expressed as quite complex human-
readable data structures, and the distribution mechanism must be able
to support that. Some Intent items will need to be flooded to most
or all nodes, and other items of Intent may only be needed by a few
nodes. Various methods could be used to distribute Intent across an
autonomic domain. One approach is to treat it like any other
technical objective needing to be synchronized across a set of nodes.
In that case the autonomic signaling protocol could be used (previous
section).
4.6. Routing
All autonomic nodes in a domain must be able to communicate with each
other, and 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.
4.7. The Autonomic Control Plane
The totality of autonomic interactions forms the "Autonomic Control
Plane". This control plane can be either implemented in the global
routing table of a node, such as IGPs in today's networks; or it can
be provided as an overlay network. The document "An Autonomic
Control Plane" ([I-D.behringer-anima-autonomic-control-plane])
describes the details.
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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.
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.
A completely autonomic way to automatically and securely deploy such
a trust infrastructure is to set up a trust anchor for the domain,
and then use an approach as in the document "Bootstrapping Key
Infrastructures" [I-D.pritikin-bootstrapping-keyinfrastructures].
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
We need to define how the fields in a domain certificate are to be
used. [tbc]
5.3. The MASA
Explain briefly the function, point to
[I-D.pritikin-bootstrapping-keyinfrastructures]. [tbc]
5.4. Sub-Domains (*)
Explain how sub-domains are handled. (tbc)
5.5. Cross-Domain Functionality (*)
Explain how trust is handled between different domains. (tbc)
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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
general concepts, such as sitting on top of the ANI, etc. Also needs
to explain that on a constrained node (see Section 3.3) not all ASAs
may run, so we have two classes of ASAs: Ones that run on an
unconstrained node, and limited function ASAs that run also on
constrained nodes. We expect unconstrained nodes to support all
ASAs.
6.2. Specific ASAs for the Enrolment Process
The following ASAs provide essential, required functionality in an
autonomic network, and are therefore mandatory to implement on
unconstrained autonomic nodes.
6.2.1. The Enrolment ASA
This section describes the function of an autonomic node to bootstrap
into the domain with the help of an enrolment proxy (see previous
section). [tbc]
6.2.2. The Enrolment Proxy ASA
This section describes the function of an autonomic node that helps a
non-enrolled, adjacent devices to enrol into the domain. [tbc]
6.2.3. The Registrar ASA
This section describes the registrar function in an autonomic
network. It explains the tasks of a registrar element, and how
registrars are placed in a network, redundancy between several, etc.
[tbc]
7. Management and Programmability
This section describes how an Autonomic Network is managed, and
programmed.
7.1. How an AN Network Is Managed
Autonomic management usually co-exists with traditional management
methods in most networks. Thus, autonomic behavior will be defined
for individual functions in most environments. In fact, the co-
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existence is twofold: autonomic functions can use traditional methods
and protocols (e.g., SNMP and NETCONF) to perform management tasks;
and autonomic functions can conflict with behavior enforced by the
same traditional methods and protocols.
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, 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 [RFC7575].
7.2. Intent (*)
This section describes Intent, and how it is managed. Intent and
Policy-Based Network Management (PBNM) is already described inside
the IETF (e.g., PCIM and SUPA) and in other SDOs (e.g., DMTF and TMF
ZOOM).
Intent can be describe 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. In an ideal autonomic domain, only one intent provided
by human administrators is necessary to operate such domain
[RFC7576]. However, it is als expected intent definition from
autonomic function(s) and even from traditional network management
elements (e.g., OSS).
Intent can be refined to lower level policies using different
approaches, such as Policy Continuum model [ref]. This is expected
in order to adapt the intent to the capabilities of managed devices.
In this context, intent may contain role or function information,
which can be translated to specific nodes [RFC7575]. One of the
possible refinements of the intent is the refinement to Event
Condition Action (ECA) rules. Such rules, which are more suitable to
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individual entities, can be defined using different syntax and
semantics.
Different parameters may be configured for intents. 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 (towards
intended operational point).
Some examples of parameters for intents are:
o Model version: The version of the model used to define the intent.
o Domain: The network scope in which the intent has effect.
o Name: The name of the intent which describes the intent for human
operators.
o Version: The version of the intent, which is primarly used to
control intent updates.
o Signature: The signature is used as a security mechanism to
provide authentication, integrity, and non-repudiation.
o Timestamp: The timestamp of the creation of the intent using the
format supported by the IETF [TBC].
o Lifetime: The lifetime in which the intent may be observed. A
special case of the lifetime is the definition of permanent
intents.
Intent distribution is considered as one of the common control and
management functions of an autonomic network [RFC7575]. Since
distribution is fundamental for autonomic networking, it is necessary
a mechanism to provision intent by all devices in a domain [draft-
carpenter-anima-gdn-protocol]. The distribution of Intent is
function of the Autonomic Control Plane and several methods can be
used to distribute Intent across an autonomic domain [draft-
behringer-anima-reference-model]. Intent distribution might not use
the ANIMA signaling protocol itself [draft-carpenter-anima-gdn-
protocol], but there is a proposal to extend such protocol for intent
delivery [draft-liu-anima-intent-distribution].
7.3. Aggregated Reporting (*)
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
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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]. The information
gathering and the reporting delivery should be done through the
autonomic control plane.
Several events can occur in an autonomic network in the same way they
can happen in a traditional network. These events can be produced
considering traditional network management protocols, such as SNMP
and syslog. However, when reporting to a human administrator, such
events should be aggregated in order to avoid advertisement 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 in order to maintain in a network-wide level which
is described in the autonomic intent.
Reporting in an autonomic network may be in the same abstraction
level of the intent. In this context, the visibility on 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. An alternative to model this is the use of
exception-based management [RFC7575].
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 can be prompted with a default action, and has the
possibility to acknowledge or override the proposed default action.
7.5. Control Loops (*)
Control loops provide a generic mechanism for self-adaptation. That
is, as user needs, business goals, and the ANI itself change, self-
adaptation enables the ANI to change the services and resources it
makes available to adapt to these changes. Self-adaptive systems
move decision-making from static, pre-defined commands to dynamic
processes computed at runtime.
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Control loops operate to continuously capture data that enables the
understanding of the system, and then provide actions to move the
state of the system toward a common goal.
7.5.1. Types of Control (*)
There are two generic types of closed loop control. Feedback control
adjusts the control loop based on measuring the output of the system
being managed to generate an error signal (the deviation of the
current state vs. its desired state). Action is then taken to reduce
the deviation.
In contrast, feedforward control anticipates future effects on a
controlled variable by measuring other variables whose values may be
more timely, and adjusts the process based on those variables. In
this approach, control is not error-based, but rather, based on
knowledge.
Autonomic control loops MAY require both feedforward and feedback
control.
7.5.2. Types of Control Loops (*)
There are many different types of control loops. In autonomics, the
most commonly cited loop is called Monitor-Analyze-Plan-Execute (with
Knowledge), called MAPE-K [Kephart03]. However, MAPE-K has a number
of systemic problems, as described in [Strassner09]. Therefore,
other autonomic architectures, such as AutoI [autoi] and FOCALE
[Strassner07] and use control loops that evolved from the OODA
(Observe-Orient-Decide-Act) control loop [Boyd95]. The reason for
using this loop, and not the MAPE-K loop, is because the OODA loop
contains a critical step not contained in other loops: orientation.
Orientation determines how observations, decisions, and actions are
performed.
Figure 6 shows a simplified model of a control loop containing both
feedforward and feedback elements.
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Input Variables
----------+-------------------------+
| |
| |
\ / \ /
+-----+------+ +----+----+
Set Point --->| Controller |------------>| Process |--+---> Output
+-----+------+ Deltas of +---------+ |
^ Control |
| Variable(s) |
| |
+---------------------------------+
Figure 6: Control Loop with Feedforward and Feedback Elements
Note that Figure 6 is a STATIC model. Figure 7 is a dynamic version,
called a Model-Reference Adaptive Control Loop (MRACL).
Model +--------------+
+-------+ Output | Adaptive |<----+
+--->| Model |--------->| Algorithm(s) | |
| +-------+ +---+-----+----+ |
| Adjusted | ^ |
Input | Parameters | | |
--------+ +----------------+ | |
| | | |
| | +---------+ |
| \ / | |
| +-----+------+ | +---------+ |
+--->| Controller |-----+------>| Process |--+---> Output
+-----+------+ Deltas of +---------+ |
^ Control |
| Variable(s) |
| |
+---------------------------------+
Figure 7: A Model-Reference Adaptive Control Loop
More complex adaptive control loops have been defined; these will be
described in a future I-D, so that an appropriate gap analysis can be
defined to recommend an architectural approach for ANIMA.
7.5.3. Management of an Autonomic Control Loop (*)
Both standard and adaptive control loops (e.g., as represented in
Figures X and X1, respectively) enable intervention by a human
administrator or central control systems, if required. Interaction
mechanisms include changing the behaviour of one or more elements in
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the control loop, as well as providing mechanisms to bypass parts of
the control loop (e.g., skip the "decide" phase and go directly to
the "action" phase of an OODA loop, as is done in FOCALE). This also
enables the default behaviour to be changed if necessary.
7.5.4. Elements of an Autonomic Control Loop (*)
An autonomic control loop MUST be able to perform the following
functions as part of its operation:
o Observe and collect data from the system being managed
o Orient these data, so that their meaning and significance can be
understood in proper context
o Analyze the collected data through filtering, correlation, and
other mechanisms to define a model of past and current states
o Plan different actions based on inferring trends, determining root
causes, and similar processes
o Decide which plan(s) to take
o Execute the plan, and then repeat these steps
In addition, an autonomic control loop SHOULD be able to execute one
or more machine learning algorithms that can learn from and make
predictions on monitored data. This enables more efficient
adaptivity. Note that machine learning is build from a model of
exemplar inputs in order to make decisions and predictions.
Supporting algorithms, such as those for data mining and analytics,
SHOULD also be supported.
7.6. APIs (*)
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. APIs
MUST be able to express and preserve semantics across different
domains.
7.6.1. Dynamic 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 (the former enables software to examine the type and
properties of an object at runtime, while the latter enables a
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program to manipulate the attributes, methods, and/or metadata of an
object.
7.6.2. APIs and Semantics(*)
An API is NOT the same as an interface.
An interface is a boundary across which different components of a
system exchange information. An API is a set of software (including
tools, protocols, and programs) for building software applications.
An API defines a set of data structures, inputs, outputs, and
operations that can be used by a programmer to build an application.
An Autonomic API must pay particular attention to semantics.
Previous designs have used the notion of a software contract to build
high-quality APIs that are distributed and modular. A software
contract [Meyer97] is 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 specification of the mutual obligations that interacting
components must respect. For example, when a method executes, the
following must hold:
o pre-conditions must be satisfied before the method can start
execution
o post-conditions must be satisfied when the method has finished
execution
o invariant attributes must not change during the execution of the
method
7.6.3. API Considerations (*)
APIs should perform one function well, not perform many different and
unrelated functions. In software design, this is called the Single
Responsibility Principle [srp]
7.7. Data Model (*)
The following definitions are taken from [supa-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
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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
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 (*)
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.
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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.
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 exists 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).
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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
9.1. Threat Analysis
This is a preliminary outline of a threat analysis, to be expanded
and made more specific as the various Autonomic Networking
specifications evolve.
Since AN will hand over responsibility for network configuration from
humans or centrally established management systems to fully
distributed devices, the threat environment is also fully
distributed. On the one hand, that means there is no single point of
failure to act as an attractive target for bad actors. On the other
hand, it means that potentially a single misbehaving autonomic device
could launch a widespread attack, by misusing the distributed AN
mechanisms. For example, a resource exhaustion attack could be
launched by a single device requesting large amounts of that resource
from all its peers, on behalf of a non-existent traffic load.
Alternatively it could simply send false information to its peers,
for example by announcing resource exhaustion when this was not the
case. If security properties are managed autonomically, a
misbehaving device could attempt a distributed attack by requesting
all its peers to reduce security protections in some way. In
general, since autonomic devices run without supervision, almost any
kind of undesirable management action could in theory be attempted by
a misbehaving device.
If it is possible for an unauthorised device to act as an autonomic
device, or for a malicious third party to inject messages appearing
to come from an autonomic device, all these same risks would apply.
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, but this would be quite limited (for example, this
would be highly unlikely to reveal any specific information about
user traffic). AN messages are liable to be exposed to third parties
on any unprotected Layer 2 link, and to insider attacks even on
protected Layer 2 links.
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10. IANA Considerations
This document requests no action by IANA.
11. Acknowledgements
Many people have provided feedback and input to this document: Sheng
Jiang, Roberta Maglione, Jonathan Hansford.
12. References
[I-D.behringer-anima-autonomic-addressing]
Behringer, M., "An Autonomic IPv6 Addressing Scheme",
draft-behringer-anima-autonomic-addressing-01 (work in
progress), June 2015.
[I-D.behringer-anima-autonomic-control-plane]
Behringer, M., Bjarnason, S., BL, B., and T. Eckert, "An
Autonomic Control Plane", draft-behringer-anima-autonomic-
control-plane-02 (work in progress), March 2015.
[I-D.carpenter-anima-gdn-protocol]
Carpenter, B. and B. Liu, "A Generic Discovery and
Negotiation Protocol for Autonomic Networking", draft-
carpenter-anima-gdn-protocol-04 (work in progress), June
2015.
[I-D.irtf-nmrg-autonomic-network-definitions]
Behringer, M., Pritikin, M., Bjarnason, S., Clemm, A.,
Carpenter, B., Jiang, S., and L. Ciavaglia, "Autonomic
Networking - Definitions and Design Goals", draft-irtf-
nmrg-autonomic-network-definitions-07 (work in progress),
March 2015.
[I-D.jiang-auto-addr-management]
Jiang, S., Carpenter, B., and Q. Qiong, "Autonomic
Networking Use Case for Auto Address Management", draft-
jiang-auto-addr-management-00 (work in progress), April
2014.
[I-D.pritikin-bootstrapping-keyinfrastructures]
Pritikin, M., Behringer, M., and S. Bjarnason,
"Bootstrapping Key Infrastructures", draft-pritikin-
bootstrapping-keyinfrastructures-01 (work in progress),
September 2014.
[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", BCP 14, RFC 2119, March 1997.
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[RFC4193] Hinden, R. and B. Haberman, "Unique Local IPv6 Unicast
Addresses", RFC 4193, October 2005.
[RFC7404] Behringer, M. and E. Vyncke, "Using Only Link-Local
Addressing inside an IPv6 Network", RFC 7404, November
2014.
[RFC7575] Behringer, M., Pritikin, M., Bjarnason, S., Clemm, A.,
Carpenter, B., Jiang, S., and L. Ciavaglia, "Autonomic
Networking: Definitions and Design Goals", RFC 7575, June
2015.
Authors' Addresses
Michael H. Behringer (editor)
Cisco Systems
Building D, 45 Allee des Ormes
Mougins 06250
France
Email: mbehring@cisco.com
Brian Carpenter
Department of Computer Science
University of Auckland
PB 92019
Auckland 1142
New Zealand
Email: brian.e.carpenter@gmail.com
Toerless Eckert
Cisco
Email: eckert@cisco.com
Laurent Ciavaglia
Alcatel Lucent
Route de Villejust
Nozay 91620
France
Email: laurent.ciavaglia@alcatel-lucent.com
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Bing Liu
Huawei Technologies
Q14, Huawei Campus
No.156 Beiqing Road
Hai-Dian District, Beijing 100095
P.R. China
Email: leo.liubing@huawei.com
Jeferson Campos Nobre
Federal University of Rio Grande do Sul
Av. Bento Goncalves, 9500
Porto Alegre 91501-970
Brazil
Email: jcnobre@inf.ufrgs.br
John Strassner
Huawei Technologies
2330 Central Expressway
Santa Clara, CA 95050
USA
Email: john.sc.strassner@huawei.com
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