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ANIMA                                                       L. Ciavaglia
Internet-Draft                                                 P. Peloso
Intended status: Informational                                     Nokia
Expires: September 22, 2016                               March 21, 2016


                Knowledge Exchange in Autonomic Networks
                 draft-ciavaglia-anima-knowledge-00.txt

Abstract

   This document describes a solution to manage the exchange and
   processing of information and knowledge between autonomic functions.
   The objective is to provide a unified interface to enable an
   interoperable management of information flows among autonomic
   functions by insuring the use common mechanisms.  The protocol
   negotiate and automatically adapt to the communication and
   information capabilities, requirements and constraints of the
   participating entities.

Requirements Language

   The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
   "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
   document are to be interpreted as described in RFC 2119 [RFC2119].

Status of This Memo

   This Internet-Draft is submitted to IETF 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 September 22, 2016.

Copyright Notice

   Copyright (c) 2016 IETF Trust and the persons identified as the
   document authors.  All rights reserved.




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   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.

   This document may not be modified, and derivative works of it may not
   be created, except to format it for publication as an RFC or to
   translate it into languages other than English.

Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
   2.  Knowledge Exchange Interface  . . . . . . . . . . . . . . . .   3
     2.1.  Information Collection and Dissemination - ICD  . . . . .   4
     2.2.  Information Storage and Indexing - ISI  . . . . . . . . .   5
     2.3.  Information Processing and Knowledge Production - IPKP  .   5
     2.4.  Information Flow Establishment and Optimization - IFEO  .   7
   3.  Acknowledgments . . . . . . . . . . . . . . . . . . . . . . .  12
   4.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .  13
   5.  Security Considerations . . . . . . . . . . . . . . . . . . .  13
   6.  References  . . . . . . . . . . . . . . . . . . . . . . . . .  13
     6.1.  Normative References  . . . . . . . . . . . . . . . . . .  13
     6.2.  Informative References  . . . . . . . . . . . . . . . . .  13
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  14

1.  Introduction

   The ANIMA autonomic management framework addresses the growing
   management complexity of the highly decentralized and dynamic
   environment of service provider networks.  The ANIMA autonomic
   management framework will help to produce the unification,
   governance, and "plug and play" for autonomic networking solutions
   within existing and future management ecosystems.  Three main
   functional blocks namely the Governance, Coordination and Knowledge
   functionalities are essential to ensure a proper management and
   interworking of Autonomic Service Agents (ASAs).  This document
   describes a solution to manage the exchange and processing of
   information and knowledge between autonomic functions.  The objective
   is to provide a unified interface to enable an interoperable
   management of information flows among autonomic functions by insuring
   the use common mechanisms.  The protocol negotiate and automatically
   adapt to the communication and information capabilities, requirements
   and constraints of the participating entities.  The Knowledge
   functionality plays the role of information / knowledge collection,
   aggregation, storage/registry, knowledge production and distribution
   across all the ANIMA functional components (i.e.  ANI and ASAs).



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   The Knowledge block is composed of the following functions:

   o  Information Collection and Dissemination - ICD

   o  Information Storage and Indexing - ISI

   o  Information Processing and Knowledge Production - IPKP

   o  Information Flow Establishment and Optimization - IFEO

   The Knowledge Block offers basic information/knowledge manipulation
   functionalities to the ANIMA entities through the Knowledge Exchange
   Interface.  A second interface, the Knowledge Management Interface,
   handles information flow management that includes configuration
   actions towards the optimal handling of the information/knowledge in
   the management system.

2.  Knowledge Exchange Interface

   An Autonomic Service Agent needs two different types of interfaces to
   deal with the exchange of knowledge.

      Knowledge Exchange Interface: Interfaces through which the
      information are actually exchanged.

      Knowledge Management Interface: Interfaces through which the
      information flows are negotiated, and information capacities are
      being discovered/advertised.  This interface provides
      configuration actions towards the optimal handling of the
      information/knowledge in the ASA.

   The most important concept is the knowledge exchange flow, which is
   being set between two knowledge exchange interfaces.  It is
   determined by the two endpoints of the flow and by the type of
   information that is being conveyed over the flow.  Some additional
   parameters define the way the information are being exchanged (Push
   or Pull mode plus additional parameters to determine the frequency
   and conditions of the actual information exchange).

   The features of the knowledge exchange flow are being negotiated by
   Knowledge Management Interfaces and possibly a third party in charge
   of optimizing the information flows over the whole system.  The
   objective of this negotiation is to determine the characteristics of
   the exchange flow, which will then be enforced between two/multiple
   knowledge exchange interfaces.






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2.1.  Information Collection and Dissemination - ICD

   The Information Collection and Dissemination (ICD) function is
   responsible for information collection, sharing, retrieval and
   dissemination.  The ASAs can act as sources or sinks of information.
   The sources subscribe to the Information Catalog by exposing the type
   of information they can produce.  On the one hand, each information
   source should subscribe information availability and the equivalent
   collection constraints (e.g., the supported granularity of
   collection).  On the other hand, each information sink should
   subscribe information retrieval requirements with a similar process.
   The subscription process takes place during the ASA bootstrapping.
   The matching of constraints with requirements takes place during an
   equivalent negotiation process.

   Information can be directly retrieved from or shared with a dedicated
   Knowledge Sharing system (a sort of ASA which roles is limited to be
   used as a store and sharing entity at the service of other ASAs).  As
   an information collection process is triggered by a component
   requesting the information, a catalog of the available information
   has to be built and kept.  This catalog indexes which ASA can produce
   which information.  Then upon a bootstrapping ASA requesting a given
   information to work, the entity in charge of this catalog would then
   inform requesting ASA of the source ASA.  This process could be
   supported by GRASP discovery mechanism.

   The information collection process may be optimized by the
   Information Flow Establishment and Optimization - IFEO or another
   utility ASA in charge of optimizing the flows.  This ASA acts as the
   third party during the negotiation phase between an information
   source and an information sink.  If many information sink need the
   same information, the negotiation entity, is liable to enforce the
   use of an intermediate Knowledge Sharing system that would collect
   the information from the source before flooding to sinks according to
   their requirements.

   The collected information may either be directed to the Information
   Processing and Knowledge Production function for a further processing
   (e.g., aggregation or knowledge production) and then optionally
   stored/indexed to the Information Storage and Indexing - ISI
   function.  The storage option may be provided or demanded based on
   the nature of the information, ASA demands, optimization goals, etc.
   After this stage, the information or produced knowledge could be
   passed back to the ICD function for dissemination.







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2.2.  Information Storage and Indexing - ISI

   The Information Storage and Indexing (ISI) function is a logical
   construct representing a distributed repository for registering ASAs,
   indexing (and optionally storing) information/knowledge.  The ISI
   function stores information, such as ASA registration information and
   knowledge.  The ISI functionality includes methods and functions for
   keeping track of information sources, including information
   registration and naming, constraints of information sources,
   information directory and indexing.  An important storage aspect,
   which can assist the knowledge production handled by the Information
   Processing and Knowledge Production function, is the inherent support
   of historical capabilities.  For example, an ASA could request
   information and/or knowledge that was stored in the past using an
   appropriate time stamp.  It should be noted that knowledge production
   functionality is not part of the ISI function, but it supports the
   storing of knowledge derived due to some earlier calculations.  The
   ISI optionally stores knowledge produced from the Information
   Processing and Knowledge Production function (for extended-scoped
   knowledge) or Knowledge Building ASAs (for locally-scoped knowledge).
   The different ANIMA entities either requesting or storing information
   to the Knowledge block, do not directly communicate with the ISI.
   The ICD function handles information collection or dissemination
   between the storage points and the ASAs.  Furthermore, ISI supports:
   (i) publish/subscribe information dissemination capabilities, (ii)
   alternative storage structures (i.e., centralized versus distributed
   or hierarchical) and database technologies based on the context, and
   (iii) information and knowledge caching.

2.3.  Information Processing and Knowledge Production - IPKP

   The Information Processing and Knowledge Production function (IPKP)
   is responsible for operations related to information processing
   (i.e., aggregation) and knowledge production.  The IPKP provides to
   ASAs and the ANIMA management functions the necessary tool kit to
   produce different information abstractions, including processed
   information and extended-scoped knowledge.  The Knowledge Production
   (KP) operation handles and produces knowledge that may be extended-
   scoped.  The latter type of knowledge is being produced out of
   aggregated information or locally-scoped knowledge.  Locally-scoped
   knowledge can be built from the Knowledge Building ASAs out of data/
   information directly collected from the managed entities, i.e., its
   scope is limited to those entities.  In all cases of knowledge
   production, reasoning and inference mechanisms are required.  These
   mechanisms are based on different techniques depending on the exact
   problem addressed, the type of inputs used and the type of output
   that needs to be acquired.  Such techniques come from scientific
   areas like statistics, clustering, reasoning, Fuzzy or machine



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   learning (including supervised, unsupervised and reinforcement
   learning techniques).  All the above information (e.g., problem
   addressed, type of inputs / outputs, inference/reasoning mechanisms
   etc) must be described in a proper ontology, ready to be looked up
   from the IPKP function when such a demand appears.  An ASA or ANIMA
   management function that requires the IPKP functionalities requests
   to utilize either an Information Aggregation (IA) or a Knowledge
   Production (KP) operation.  The ICD function handles the
   communication of the ANIMA management component with the internal
   IPKP functionalities and the IPKP controller is responsible to
   control the internal IPKP components.  The two IPKP operations (i.e.,
   information aggregation and knowledge production) require a number of
   basic steps:

      Step 1: Determining the information aggregation or knowledge
      production parameters (e.g., information filtering configuration,
      the inference/reasoning algorithm to use, translation
      requirements, whether aggregation is required and/or information/
      knowledge post-processing requirements).  This process is being
      handled from the IPKP controller, which matches the ANIMA
      component's requirements and the type of problem to solve with the
      relevant information.  The parameters are being communicated to
      all relevant internal IPKP components.

      Step 2: Collection of input information either from an ANIMA
      component that produces it or from the ISI function (i.e., the
      knowledge storage).  A collection request is being passed back
      from the IPKP controller to the ICD function.

      Step 3: Pre-processing of the input information (e.g., applying
      information filtering) that may be required.  The pre-processing
      requirements are being set from the IPKP controller.

      Step 4: The input information is being passed to the IA operation
      in case of information aggregation, where an aggregation process
      takes place according the requirements (e.g., aggregation function
      used) being set from the IPKP controller.  In case of knowledge
      production, this step may be bypassed or not (i.e., the higher-
      level knowledge production processes may require aggregation
      before the inference/reasoning process).

      Step 5: In case of knowledge production, the input information may
      need to be translated in a convenient representation, e.g., to
      OWL.  The translation configuration is being set from the IPKP
      controller to match the requirements of the inference/reasoning
      mechanism identified from the (TBD) ANIMA ontology.





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      Step 6: The actual inference/reasoning process takes place in this
      step.  The input information (i.e., in an appropriate form) and
      the relevant knowledge production rules are being passed to the
      identified inference/reasoning mechanism.  A rule description
      language that can be used is the Semantic Web Rule Language
      (SWRL).  The output of this process is the produced Knowledge.
      This step may be bypassed, in case of a request for information
      aggregation without knowledge production.

      Step 7: The produced knowledge or aggregated information may need
      a post-processing (e.g., filtering).  This step is optional.

      Step 8: At this stage, the result is being communicated to the ICD
      function, to find its way to the requesting ANIMA component.  The
      produced knowledge or aggregated information can be optionally
      stored in the ISI function so as to be available for ANIMA
      management mechanisms or ASAs when requested/needed.

2.4.  Information Flow Establishment and Optimization - IFEO

   The information flow negotiation and optimization aspects are crucial
   processes overseen from the Information Flow Establishment and
   Optimization (IFEO) function.  The IFEO function, besides organizing
   internal optimization aspects (e.g., setting filtering or information
   accuracy objectives), also regulates the information flow based on
   the current state and the locations of the participating ANIMA
   components (e.g., the ASAs producing or requiring information).  All
   relevant communication between the knowledge functions and the ANIMA
   components takes place through the Knowledge Management interface,
   unless it is otherwise stated.

   For clarity purposes, we define the specifications of the IFEO
   function along with a representative example.  We assume the
   following two ASAs: (a) the Virtual Infrastructure Management (VIM)
   ASA that provides management and control facilities for virtual
   infrastructures, including support of traffic monitoring; and (b) the
   Placement Optimization (PO) ASA that optimizes the data flow over a
   virtual network through adapting the positioning of communicating
   nodes (e.g., data servers) in response to the dynamic network
   conditions.  In this example, the VIM ASA provides traffic monitoring
   information from a particular virtual network to the PO ASA.  The PO
   ASA takes optimization decisions for the network based on this
   information, i.e., repositions communicating nodes in order to
   optimize network communication.  The information flow negotiation and
   optimization processes include a number of basic phases, elaborated
   below:





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      Phase 1 - Registration: In this phase the ASAs, as part of their
      registration process with the knowledge block (i.e., described in
      section 3.6.2), will communicate the following information to the
      knowledge:

         Information they can offer instantly or after an information
         collection process.

         Knowledge they can offer instantly or after a knowledge
         production process.

         Information/knowledge they would require (mandatory or
         optional).

      The above information is embedded in the description of the ASA
      instance description.  In our example, the VIM ASA registers the
      information it can offer (e.g., the topology information and
      measurements on the link loads).  This information can be offered
      instantly (i.e., does not require an information collection
      process to start, since it monitors the network continuously).
      The PO ASA registers the same information type as mandatory
      information required.

      Phase 2 - An ANIMA management function requesting knowledge: In
      this phase a process in an ANIMA management function (like a
      supervision process in management or a knowledge production
      mechanism or a coordination mechanism) demands to register to a
      given piece of information produced by a given ASA.  This
      information is expressed as a information specification.  In that
      case, the Knowledge Management Interface of the requesting entity
      is calling a TBD knowledge method named to request the registered
      information.

      Phase 3 - Information Flow Negotiation: In the third phase, the
      knowledge block through its IFEO function handles a flow
      negotiation process between entities (i.e., ASAs or management
      mechanisms) requiring information and those can provide it.  The
      two entities exchange information flow related parameters with the
      knowledge block, in order to confirm that all information-related
      requirements can be satisfied under the given constraints.  An
      information flow is either established between the two entities
      directly or between an entity and the knowledge block itself, in
      case the requested information is available in the knowledge
      storage.  The negotiation process includes flow-level optimization
      aspects as well.  This phase is composed of the following steps:

         Step 1 - Preselecting the information flow ends: Whenever a ASA
         registers it advertises requested information/knowledge (under



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         a specific format TBD), the knowledge block fetches in its
         indexing storage the appropriate entity (ASA or management
         mechanism) that can produce the requested information/
         knowledge.  It may either select an entity by considering the
         type of information/knowledge required or, in case of
         alternative options, assign the first entity it finds and
         enlist the other potential choices in a queue.  In case the
         required information is in the knowledge storage, an
         information flow is created with the knowledge.  The same
         process happens when a ANIMA management entity requests some
         knowledge, depending on the form of the request (i.e., a
         fetching from the indexing storage may or may not be required):

            ASA information: already specifies which is the Instance ID
            of the ASA producing the information.

            ANIMA information: a fetching from the index table is
            required to pick the appropriate flow ends.

            Management information: then the fetching does not concern
            finding a flow end, but finding all the flow ends matching
            the pattern provided by the management information in order
            to establish as many flows as indexed ANIMA information
            objects inheriting from the management information (this
            corresponds to a supervision mechanism requesting to
            register to ASA utilities, hence a flow for each ASA capable
            of advertising its utility will be created).  Reversely,
            knowledge may have postponed flow establishments of some
            requested information because at the time the request was
            received, no entity producing this information was
            registered.  In that case, knowledge checks with every
            received instance description whether the advertised
            information matches previously unsolved requests.  After
            that, the IFEO proceeds to Step 2.  In the studied example,
            the knowledge block preselects the VIM ASA as information
            source for the PO ASA that acts as the information sink.
            This selection was based on the matching information URIs
            referenced in the registered ANIMA information data
            structures from the two ASAs.

         Step 2 -Communicating the negotiation parameters: in step 2, a
         negotiation process is initiated between the entity requiring
         information/knowledge (i.e., the information sink entity) and
         the selected information source entity.  The negotiation begins
         with the two entities communicating additional negotiation
         parameters to the knowledge block.  Specifically, the
         information sink entity communicates an augmented version of
         the ANIMA information with:



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            -QoS Requirements on the information/knowledge it requires.

            -Preferred information communication method (i.e., either
            push/pull or pub/sub).

            -List of Knowledge Exchange interfaces (addresses) on which
            the information can be received and possibly an internal
            metric regarding the internal costs to use this information
            from each of these interfaces.

            -REST callback functions that may be required at this end of
            communication (e.g., in case of an information subscribe
            method).

         In a similar way, the information source entity communicates
         the following to the knowledge block:

            -QoS Constraints on the information/knowledge it can offer.

            -Supported(and preferred) information communication method
            (i.e., either push/pull or pub/sub).

            -Whether for this requested information/knowledge an
            "information collection/knowledge production" process is
            already activated or needs to be initiated.

            -List of Knowledge Exchange interfaces (addresses) on which
            the information can be provided and possibly an internal
            metric regarding their internal cost to bring this
            information up to the interface.

            -REST Callback functions for the relevant capabilities
            (i.e., triggering functions for information collection or
            knowledge production - if relevant).

         Practically, the knowledge block initiates a new negotiation
         with the execution of the sink and source parameters
         negotiation methods of the Knowledge Management Interface.
         Both methods take as input the specifications of the
         information to be communicated from the established
         communication flow, represented as an ANIMA information data
         structure.  In the reference scenario, the VIM ASA communicates
         to the knowledge: (i) the QoS constraints of the topology and
         link load information it can offer, e.g., monitors information
         once per 10 secs, and (ii) a number of available Knowledge
         Exchange interfaces that can provide the information.  The PO
         ASA communicates to the knowledge: (i) the QoS requirements of
         the required information, e.g., once per 30 secs, and (ii) a



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         number of available Knowledge Exchange interfaces that can
         receive the information.

         Step 3 - Completing the negotiation: The knowledge block
         matches information flow requirements with constraints,
         determines the information flow parameters with flow
         optimization considerations and then issues a Knowledge
         Exchange Policy summarizing an information flow contract to
         both entities. knowledge also stores the Knowledge Exchange
         Policy through the Information Flow Configuration and
         Statistics operation of the IFEO function.  In case of an
         unsuccessful negotiation (i.e., the requirements do not match
         the constraints), it may disengage or trigger a new
         negotiation:

            a) With the same information source entity but lower
            requirements.

            >b) With another information source entity that waits in the
            queue, until the queue is exhausted.

         The Information Exchange Policies for the corresponding flow
         are being produced from the Information Quality Controller
         operation of the IFEO knowledge block function and include:

            -Location/addresses of the participating Knowledge Exchange
            Interfaces in the information flow.

            -Internal knowledge optimization decisions that may impact
            the information flow (e.g., optimal knowledge aggregation/
            storage points), in case the knowledge block is the one end
            of the flow.

            -Addresses of triggering callback functions for knowledge
            production or information collection - if relevant.

   These policies are considering the requirements/constraints of the
   participating entities and the global performance objective coming
   from the operator (e.g. via the ANIMA Intent Policy).  The knowledge
   establishes the information flow using a set flow method of the
   Knowledge Management Interface, that takes as an input the decided
   Information Flow Exchange Policies, represented as a flow data
   structure.

   The decided Information Exchange Policies are being applied to the
   network through the respective ASAs or communicated to the knowledge
   functions they are associated with.  Since the appropriate context
   environment for the new information flow is prepared, a suitable path



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   between the participating nodes is established next.  This process
   considers the locations of the entities producing and requiring
   information and the required knowledge nodes (e.g., aggregation
   points, storage points etc) as well as the potential traffic
   characteristics.  After that, the Knowledge Exchange interface can be
   accessed anytime from the information sink entity to receive the
   needed information/knowledge.  In our reference scenario, the
   knowledge block matches the information flow constraints (e.g.,
   supported monitoring rate) of the VIM ASA with the information flow
   requirements from the PO ASA.  Then it selects the most appropriate
   Knowledge Exchange interfaces to communicate the information from the
   VIM to the PO ASA.  A new information flow contract is established
   and communicated to the two ASAs and stored in the knowledge block.
   The information flow is established and the PO ASA can retrieve the
   required information from the VIM ASA via the appropriate Knowledge
   Exchange interface.  The PO ASA can now begin taking network
   optimization decisions using that information.

   Knowledge-level Optimization: Furthermore, knowledge supports a
   global optimization process that is triggered periodically or when a
   global performance objective change is requested from the GOV.  This
   process takes optimization decisions using the aggregated information
   from the configuration of all established information flows and is
   related with a restructuring of the knowledge functions themselves.
   In other words, global-optimization algorithms may discard or update
   Knowledge Exchange Policies enforced for established information
   flows.  It takes as an input the global picture of all the
   established information flow contacts and provides as an output
   different contracts aligned better to the new updated demands (e.g.,
   a new received global objective).  This process may initiate re-
   negotiations that include requesting again from the entities what
   their requirements and capabilities are.  For example, the
   distributed knowledge nodes may be increased, decreased or
   repositioned in order to accommodate all established information
   flows and the global optimization goal better.  The optimization
   process is triggered by the IFEO function and regulates the
   information flow based on the current state and the locations of the
   participating ANIMA components.  In particular, the IFEO controls
   information collection handled from the ICD function, information
   aggregation, and aggregation node placement.  Furthermore, it guides
   a filtering system for information collection and aggregation points
   that can significantly reduce the communication overhead.

3.  Acknowledgments

   This draft was written using the xml2rfc project.





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   The content of this draft builds upon work achieved during the EU FP7
   UniverSelf project (www.univerself-project.eu).

4.  IANA Considerations

   This memo includes no request to IANA.

5.  Security Considerations

   TBD

6.  References

6.1.  Normative References

   [I-D.ciavaglia-anima-coordination]
              Ciavaglia, L. and P. Peloso, "Autonomic Functions
              Coordination", draft-ciavaglia-anima-coordination-00 (work
              in progress), July 2015.

   [I-D.peloso-anima-autonomic-function]
              Peloso, P. and L. Ciavaglia, "A Day in the Life of an
              Autonomic Function", draft-peloso-anima-autonomic-
              function-00 (work in progress), October 2015.

   [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>.

6.2.  Informative References

   [I-D.behringer-anima-reference-model]
              Behringer, M., Carpenter, B., Eckert, T., Ciavaglia, L.,
              Liu, B., Jeff, J., and J. Strassner, "A Reference Model
              for Autonomic Networking", draft-behringer-anima-
              reference-model-04 (work in progress), October 2015.

   [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>.








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Authors' Addresses

   Laurent Ciavaglia
   Nokia
   Villarceaux
   Nozay  91460
   FR

   Email: laurent.ciavaglia@nokia.com


   Pierre Peloso
   Nokia
   Villarceaux
   Nozay  91460
   FR

   Email: pierre.peloso@nokia.com

































Ciavaglia & Peloso     Expires September 22, 2016              [Page 14]


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