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ANIMA                                                  M. Behringer, Ed.
Internet-Draft                                             Cisco Systems
Intended status: Informational                              B. Carpenter
Expires: April 18, 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
                                                        October 16, 2015


               A Reference Model for Autonomic Networking
                draft-behringer-anima-reference-model-04

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
   Task Force (IETF).  Note that other groups may also distribute
   working documents as Internet-Drafts.  The list of current Internet-
   Drafts is at http://datatracker.ietf.org/drafts/current/.

   Internet-Drafts are draft documents valid for a maximum of six months
   and may be updated, replaced, or obsoleted by other documents at any
   time.  It is inappropriate to use Internet-Drafts as reference
   material or to cite them other than as "work in progress."

   This Internet-Draft will expire on April 18, 2016.







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Copyright Notice

   Copyright (c) 2015 IETF Trust and the persons identified as the
   document authors.  All rights reserved.

   This document is subject to BCP 78 and the IETF Trust's Legal
   Provisions Relating to IETF Documents
   (http://trustee.ietf.org/license-info) in effect on the date of
   publication of this document.  Please review these documents
   carefully, as they describe your rights and restrictions with respect
   to this document.  Code Components extracted from this document must
   include Simplified BSD License text as described in Section 4.e of
   the Trust Legal Provisions and are provided without warranty as
   described in the Simplified BSD License.

Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   3
   2.  The Network View  . . . . . . . . . . . . . . . . . . . . . .   3
   3.  The Autonomic Network Element . . . . . . . . . . . . . . . .   4
     3.1.  Architecture  . . . . . . . . . . . . . . . . . . . . . .   4
   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
     4.3.  Discovery . . . . . . . . . . . . . . . . . . . . . . . .   9
     4.4.  Signaling Between Autonomic Nodes . . . . . . . . . . . .   9
     4.5.  Intent Distribution . . . . . . . . . . . . . . . . . . .  10
     4.6.  Routing . . . . . . . . . . . . . . . . . . . . . . . . .  10
     4.7.  The Autonomic Control Plane . . . . . . . . . . . . . . .  10
   5.  Functional Overview . . . . . . . . . . . . . . . . . . . . .  11
   6.  Security and Trust Infrastructure . . . . . . . . . . . . . .  13
     6.1.  Public Key Infrastructure . . . . . . . . . . . . . . . .  13
     6.2.  Domain Certificate  . . . . . . . . . . . . . . . . . . .  13
     6.3.  The MASA  . . . . . . . . . . . . . . . . . . . . . . . .  13
     6.4.  Sub-Domains (*) . . . . . . . . . . . . . . . . . . . . .  13
     6.5.  Cross-Domain Functionality (*)  . . . . . . . . . . . . .  13
   7.  Autonomic Service Agents (ASA)  . . . . . . . . . . . . . . .  14
     7.1.  General Description of an ASA . . . . . . . . . . . . . .  14
     7.2.  Specific ASAs for the Enrolment Process . . . . . . . . .  14
       7.2.1.  The Enrolment ASA . . . . . . . . . . . . . . . . . .  14
       7.2.2.  The Enrolment Proxy ASA . . . . . . . . . . . . . . .  14
       7.2.3.  The Registrar ASA . . . . . . . . . . . . . . . . . .  14
   8.  Management and Programmability  . . . . . . . . . . . . . . .  14
     8.1.  How an AN Network Is Managed  . . . . . . . . . . . . . .  14
     8.2.  Intent (*)  . . . . . . . . . . . . . . . . . . . . . . .  15
     8.3.  Aggregated Reporting (*)  . . . . . . . . . . . . . . . .  16



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     8.4.  Feedback Loops to NOC(*)  . . . . . . . . . . . . . . . .  17
     8.5.  Control Loops (*) . . . . . . . . . . . . . . . . . . . .  17
     8.6.  APIs (*)  . . . . . . . . . . . . . . . . . . . . . . . .  18
     8.7.  Data Model (*)  . . . . . . . . . . . . . . . . . . . . .  18
   9.  Coordination Between Autonomic Functions (*)  . . . . . . . .  19
     9.1.  The Coordination Problem (*)  . . . . . . . . . . . . . .  19
     9.2.  A Coordination Functional Block (*) . . . . . . . . . . .  20
   10. Security Considerations . . . . . . . . . . . . . . . . . . .  21
     10.1.  Threat Analysis  . . . . . . . . . . . . . . . . . . . .  21
   11. IANA Considerations . . . . . . . . . . . . . . . . . . . . .  22
   12. Acknowledgements  . . . . . . . . . . . . . . . . . . . . . .  22
   13. References  . . . . . . . . . . . . . . . . . . . . . . . . .  22
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  23

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
   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



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   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
   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 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, and feedback loops.  Fundamentally, there are two levels



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   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                               |
   |    - Autonomic Node Addressing                             |
   |    - 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
   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.





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   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.

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?

   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.



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         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
         "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.



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   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), 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  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.



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   o  Upgradability: The scheme must be able to support different
      addressing concepts in the future.

   The primary use for the autonomically managed addressing described
   here is for the Autonomic Control Plane
   ([I-D.ietf-anima-autonomic-control-plane]).  The fundamental
   concepts, as well as the proposed addressing scheme for the ACP is
   discussed in [I-D.behringer-anima-autonomic-addressing].

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



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   specific use case might choose their own signaling protocol, as long
   as it fits the overall security model.  However, in the general case,
   any pair of autonomic nodes might need to communicate, so there needs
   to be a generic protocol for this.  A prerequisite for this is that
   autonomic nodes can discover each other without any preconfiguration,
   as mentioned above.  To be generic, discovery and signaling must be
   able to handle any sort of technical objective, including ones that
   require complex data structures.  The document "A Generic Discovery
   and Negotiation Protocol for Autonomic Networking"
   [I-D.ietf-anima-grasp] 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 8.2 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.ietf-anima-autonomic-control-plane]) describes
   the details.






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5.  Functional Overview

   This section provides an overview on how the functions in the
   Autonomic Networking Infrastructure work together, and how the
   various documents about AN relate to each other.

   The foundations of Autonomic Networking, definitions and gap analysis
   in the context of the IETF are described in [RFC7575] and [RFC7576].

   Autonomic Networking is based on direct interactions between devices
   of a domain.  The Autonomic Networking Infrastructure (ANI) is
   normally built 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
   information about the validity and trust of the adjacent autonomic
   node's certificate, although all autonomic interactions must verify
   validity and trust independently.

   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.  This is described in
      [I-D.ietf-anima-grasp].

   o  Vendor re-direct: A new device may receive information on where
      its home network is through a vendor based MASA re-direct; this is
      typically a routable address.  See
      [I-D.pritikin-bootstrapping-keyinfrastructures].

   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.




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      As this pertains to new devices, it is covered in Section 5.3 of
      [I-D.pritikin-bootstrapping-keyinfrastructures].

   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 vendor MASA re-direct, which will be entered into the adjacency
      table (see second bullet above).  See
      [I-D.pritikin-bootstrapping-keyinfrastructures].

   o  If the node has bootstrapped into a domain (i.e., has a domain
      certificate), it will act as a proxy for neighboring nodes that
      need to be bootstrapped.  See
      [I-D.pritikin-bootstrapping-keyinfrastructures].

   o  If the adjacent node has the same domain, it will authenticate
      that adjacent node and establish the Autonomic Control Plane
      (ACP).  See [I-D.ietf-anima-autonomic-control-plane].

   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 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.






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6.  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].

6.1.  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.

6.2.  Domain Certificate

   We need to define how the fields in a domain certificate are to be
   used. [tbc]

6.3.  The MASA

   Explain briefly the function, point to
   [I-D.pritikin-bootstrapping-keyinfrastructures]. [tbc]

6.4.  Sub-Domains (*)

   Explain how sub-domains are handled. (tbc)

6.5.  Cross-Domain Functionality (*)

   Explain how trust is handled between different domains. (tbc)







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7.  Autonomic Service Agents (ASA)

   This section describes how autonomic services run on top of the
   Autonomic Networking Infrastructure.

7.1.  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 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.

7.2.  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.

7.2.1.  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]

7.2.2.  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]

7.2.3.  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]

8.  Management and Programmability

   This section describes how an Autonomic Network is managed, and
   programmed.

8.1.  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-
   existence is twofold: autonomic functions can use traditional methods



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   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].

8.2.  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
   individual entities, can be defined using different syntax and
   semantics.



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   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
   [I-D.ietf-anima-grasp].  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 [I-D.ietf-anima-grasp], but there is a
   proposal to extend such protocol for intent delivery [draft-liu-
   anima-intent-distribution].

8.3.  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
   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



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   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].

8.4.  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.

8.5.  Control Loops (*)

   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 System
   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.



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   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.

   The document [draft-strassner-anima-control-loop] defines the
   requirements for an autonomic control loop, describes different types
   of control loops, and explains how control loops are used in an
   autonomic system.

8.6.  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.

   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 semantics across different
   domains.  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:

   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

8.7.  Data Model (*)

   The following definitions are taken from [supa-model]:





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   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
   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.

9.  Coordination Between Autonomic Functions (*)

9.1.  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.



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   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.

   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.

9.2.  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.



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   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.

10.  Security Considerations

10.1.  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




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   on any unprotected Layer 2 link, and to insider attacks even on
   protected Layer 2 links.

11.  IANA Considerations

   This document requests no action by IANA.

12.  Acknowledgements

   Many people have provided feedback and input to this document: Sheng
   Jiang, Roberta Maglione, Jonathan Hansford.

13.  References

   [I-D.behringer-anima-autonomic-addressing]
              Behringer, M., "An Autonomic IPv6 Addressing Scheme",
              draft-behringer-anima-autonomic-addressing-02 (work in
              progress), October 2015.

   [I-D.ietf-anima-autonomic-control-plane]
              Behringer, M., Bjarnason, S., BL, B., and T. Eckert, "An
              Autonomic Control Plane", draft-ietf-anima-autonomic-
              control-plane-01 (work in progress), October 2015.

   [I-D.ietf-anima-grasp]
              Bormann, C., Carpenter, B., and B. Liu, "A Generic
              Autonomic Signaling Protocol (GRASP)", draft-ietf-anima-
              grasp-01 (work in progress), October 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,
              DOI 10.17487/RFC2119, March 1997,
              <http://www.rfc-editor.org/info/rfc2119>.






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   [RFC4193]  Hinden, R. and B. Haberman, "Unique Local IPv6 Unicast
              Addresses", RFC 4193, DOI 10.17487/RFC4193, October 2005,
              <http://www.rfc-editor.org/info/rfc4193>.

   [RFC7404]  Behringer, M. and E. Vyncke, "Using Only Link-Local
              Addressing inside an IPv6 Network", RFC 7404,
              DOI 10.17487/RFC7404, November 2014,
              <http://www.rfc-editor.org/info/rfc7404>.

   [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,
              <http://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,
              <http://www.rfc-editor.org/info/rfc7576>.

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






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   Laurent Ciavaglia
   Alcatel Lucent
   Route de Villejust
   Nozay  91620
   France

   Email: laurent.ciavaglia@alcatel-lucent.com


   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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