ANIMA                                                  M. Behringer, Ed.
Internet-Draft                                             Cisco Systems
Intended status: Informational                              B. Carpenter
Expires: January 1, 2016                               Univ. of Auckland
                                                               T. Eckert
                                                            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


   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

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   This Internet-Draft will expire on January 1, 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
   ( in effect on the date of
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   described in the Simplified BSD License.

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

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

         [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

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-

         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

   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

         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

   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

4.2.2.  The Base Addressing Scheme

   The Base ULA addressing scheme for autonomic nodes has the following

     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

   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.  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

   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

   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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             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, 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 "" and
   "" 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

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

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

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.

7.  Management and Programmability

   This section describes how an Autonomic Network is managed, and

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

   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

   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

   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

   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

   Autonomic control loops MAY require both feedforward and feedback

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

   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

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

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

   o  post-conditions must be satisfied when the method has finished

   o  invariant attributes must not change during the execution of the

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

   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

   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

   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

   A common coordination function requires:

   o  A common description of autonomic functions, their attributes and

   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

              Behringer, M., "An Autonomic IPv6 Addressing Scheme",
              draft-behringer-anima-autonomic-addressing-01 (work in
              progress), June 2015.

              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.

              Carpenter, B. and B. Liu, "A Generic Discovery and
              Negotiation Protocol for Autonomic Networking", draft-
              carpenter-anima-gdn-protocol-04 (work in progress), June

              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.

              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

              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

   [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

Authors' Addresses

   Michael H. Behringer (editor)
   Cisco Systems
   Building D, 45 Allee des Ormes
   Mougins  06250


   Brian Carpenter
   Department of Computer Science
   University of Auckland
   PB 92019
   Auckland  1142
   New Zealand


   Toerless Eckert


   Laurent Ciavaglia
   Alcatel Lucent
   Route de Villejust
   Nozay  91620


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   Bing Liu
   Huawei Technologies
   Q14, Huawei Campus
   No.156 Beiqing Road
   Hai-Dian District, Beijing  100095
   P.R. China


   Jeferson Campos Nobre
   Federal University of Rio Grande do Sul
   Av. Bento Goncalves, 9500
   Porto Alegre  91501-970


   John Strassner
   Huawei Technologies
   2330 Central Expressway
   Santa Clara, CA  95050


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