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ANIMA WG                                                   CJ. Bernardos
Internet-Draft                                                      UC3M
Intended status: Experimental                                  A. Mourad
Expires: September 11, 2019                                 InterDigital
                                                          March 10, 2019


                Autonomic setup of fog monitoring agents
                draft-bernardos-anima-fog-monitoring-00

Abstract

   The concept of fog computing has emerged driven by the Internet of
   Things (IoT) due to the need of handling the data generated from the
   end-user devices.  The term fog is referred to any networked
   computational resource in the continuum between things and cloud.  In
   fog computing, functions can be stiched together composing a service
   function chain.  These functions might be hosted on resources that
   are inherently heterogeneous, volatile and mobile.  This means that
   resources might appear and disappear, and the connectivity
   characteristics between these resources may also change dynamically.
   This calls for new orchestration solutions able to cope with dynamic
   changes to the resources in runtime or ahead of time (in anticipation
   through prediction) as opposed to today's solutions which are
   inherently reactive and static or semi-static.

   A fog monitoring solution can be used to help predicting events so an
   action can be taken before an event actually takes place.  This
   solution is composed of agents running on the fog nodes plus a
   controller hosted at another device (running in the infrastructure or
   in another fog node).  Since fog environments are inherently volatile
   and extremely dynamic, it is convenient to enable the use of
   autonomic technologies to autonomously set-up the fog monitoring
   platform.  This document aims at presenting this use case as well as
   specifying how to use GRASP as needed in this scenario.

Status of This Memo

   This Internet-Draft is submitted in full conformance with the
   provisions of BCP 78 and BCP 79.

   Internet-Drafts are working documents of the Internet Engineering
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   Internet-Drafts are draft documents valid for a maximum of six months
   and may be updated, replaced, or obsoleted by other documents at any



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   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 11, 2019.

Copyright Notice

   Copyright (c) 2019 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
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   include Simplified BSD License text as described in Section 4.e of
   the Trust Legal Provisions and are provided without warranty as
   described in the Simplified BSD License.

Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
     1.1.  Problem statement . . . . . . . . . . . . . . . . . . . .   3
     1.2.  Fog monitoring framework  . . . . . . . . . . . . . . . .   4
   2.  Terminology . . . . . . . . . . . . . . . . . . . . . . . . .   5
   3.  Autonomic setup of fog monitoring framework . . . . . . . . .   6
   4.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .  10
   5.  Security Considerations . . . . . . . . . . . . . . . . . . .  10
   6.  Acknowledgments . . . . . . . . . . . . . . . . . . . . . . .  10
   7.  References  . . . . . . . . . . . . . . . . . . . . . . . . .  10
     7.1.  Normative References  . . . . . . . . . . . . . . . . . .  10
     7.2.  Informative References  . . . . . . . . . . . . . . . . .  10
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  11

1.  Introduction

   The concept of fog computing has emerged driven by the Internet of
   Things (IoT) due to the need of handling the data generated from the
   end-user devices.  The term fog is referred to any networked
   computational resource in the continuum between things and cloud.  A
   fog node may therefore be an infrastructure network node such as an
   eNodeB or gNodeB, an edge server, a customer premises equipment
   (CPE), or even a user equipment (UE) terminal node such as a laptop,
   a smartphone, or a computing unit on-board a vehicle, robot or drone.

   In fog computing, functions might be organized in service function
   chains (SFCs), hosted on resources that are inherently heterogeneous,



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   volatile and mobile.  This means that resources might appear and
   disappear, and the connectivity characteristics between these
   resources may also change dynamically.  This calls for new
   orchestration solutions able to cope with dynamic changes to the
   resources in runtime or ahead of time (in anticipation through
   prediction) as opposed to today's solutions which are inherently
   reactive and static or semi-static.

1.1.  Problem statement

   Figure 1 shows an exemplary scenario of a (robot) network service.  A
   robot device has its (navigation) control application running in the
   fog away from the robot, as a network service in the form of an SFC
   "F1-F2" (e.g., F1 might be in charge of identifying obstacles and F2
   takes decisions on the robot navigation).  Initially the function F1
   is assumed to be hosted at a fog node A and F2 at fog node B.  At a
   given point of time, fog node A becomes unavailable (e.g., due to low
   battery issues or the fog node A moving away from the coverage of the
   robot).  There is therefore a need to predict the need of migrating/
   moving the function F1 to another node (e.g., fog node C in the
   figure), and this needs to be done prior to the fog/edge node
   becoming no longer capable/available.  Such dynamic migration cannot
   be dealt with in today's orchestration solutions, which are rather
   reactive and static or semi-static (e.g., resources may fail, but
   this is an exceptional event, happening with low frequency, and only
   scaling actions are supported to react to SLA-related events).

              --------------
              |    ====    |
             ------+F1+----------
            / |  | ==== |  |     \
           /  |  +------+  |      \
           |  | fog node C |       \
           |  --------------        \
           |                         \
           |       --------------  ---\----------
           |       |    ====    |  |   \====    |
           | -----------+F1+------------+F2|    |
           |/      |  | ==== |  |  |  | ==== |  |
           o       |  +------+  |  |  +------+  |
           |       | fog node A |  | fog node B |
   --------+-      --------------  --------------
   |        |
   --0----0--

                        Figure 1: Example scenario





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   Existing frameworks rely on monitoring platforms that react to
   resource failure events and ensure that negotiated SLAs are met.
   However these are not designed to predict events likely to happen in
   a volatile fog environment, such as resources moving away, resources
   becoming unavailable due to battery issues or just changes in
   availability of the resources because of variations of the use of the
   local resources on the nodes.  Besides, it is not feasible in this
   kind of volatile and extremely mobile environment to perform a
   continuous monitoring and reporting of every possible parameter on
   all the nodes hosting resources, as this would not scale and would
   consume many resources and generate extra overhead.

   In volatile and mobile environments, prediction (make-before-break)
   is needed, as pure reaction (break-before-make) is not enough.  This
   prediction is not generic, and depends on the nature of the network
   service/SFC: the functions of the SFC, the connectivity between them,
   the service-specific requirements, etc.  Monitoring has to be setup
   differently on the nodes, depending on the specifics of the network
   service.  Besides, in order to act proactively and predict what might
   need to be done, monitoring in such a volatile and mobile
   environments does not only involve the nodes currently hosting the
   resources running the network service/service function chain (i.e.,
   hosting a function), but also other nodes which are potential
   candidates to join either in addition or in substitution to current
   nodes for running the network service in accordance with the
   orchestration decisions.

   In the example of Figure 1, the fog node initially hosting function
   F1 (fog node A) might be running out of battery and this should be
   detected before the node A actually becomes unavailable, so the
   function F1 can be effectively migrated in a time to a different fog
   node C, capable of meeting the requirements of F1 (compute,
   networking, location, expected availability, etc.).  In order to be
   able to predict the need for such a migration and have already
   identified a target fog node where to move the function, it is needed
   to have a monitoring solution in place that instructs each node
   involved in the service (A and B), and also neighboring node
   candidate (C) to host function (F1), to monitor and report on metrics
   that are relevant for the specific network service "F1-F2" that is
   currently running.

1.2.  Fog monitoring framework

   Fog environments differ from data-center ones on three key aspects:
   heterogeneity, volatility and mobility.  The fog monitoring framework
   is used to predict events triggering and orchestration event (e.g.,
   migrating a function to a different resource).




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   The monitoring framework we propose for fog environments is composed
   of 2 logical components:

   o  Fog agents running on each fog node.  An agent is responsible for
      sending information to a fog monitoring controller and to other
      fog agents.  What to monitor and what information to send
      (including frequency) is configured per agent considering the
      specifics of the network service/SFC.  A fog agent might also take
      some autonomous actions (such as request migration of a function
      to a neighbor node) in certain situations where connectivity with
      the fog monitoring controller is temporarily unavailable.

   o  A fog monitoring controller (e.g., running at the edge or at a fog
      node).  This node obtains input from the orchestration logic (MANO
      stack) and autonomously decides what information to monitor, where
      and how, based on the requirements provided by the orchestration
      logic managing the network services instantiated in the fog.  This
      configuration is network service/function specific.

      *  It interacts with the orchestration logic to coordinate and
         trigger orchestration events, such as function migration,
         connectivity updates, etc.  In some deployments, this entity
         might be co-located with the orchestration logic (e.g., the
         NFVO).

      *  It interacts with the fog agents to instruct what information
         and parameters need to be monitored, as well as to obtain such
         information.  This interaction is not limited to fog agents at
         nodes currently involved in a given network service/SFC, but
         also includes other nodes that are suitable for hosting a
         function that needs to be migrated.  This allows to provide the
         orchestration logic with candidate nodes in a pro-active way.

      *  It is capable of autonomously discover and set up fog agents.

2.  Terminology

   The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
   "SHOULD", "SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED", "MAY",
   and"OPTIONAL" in this document are to be interpreted as described in
   BCP 14 [RFC2119] [RFC8174] when, and only when, they appear in all
   capitals, as shown here.

   The following terms are using in ths document:

   fog:          Fog goes to the Extreme Edge, that is the closest
                 possible to the user including on the user device
                 itself.



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   fog node:     Any device that is capable of participating in the Fog.
                 A Fog node might be volatile, mobile and constrained
                 (in terms of computing resources).  Fog nodes may be
                 heterogeneous and may belong to different owners.

   orchestrator: In this document we use orchestrator and NFVO terms
                 interchangeably.

3.  Autonomic setup of fog monitoring framework

   Fog nodes autonomously start fog agents at the bootstrapping, then
   start looking for other agents and the fog monitoring controller.
   This autonomic setup can be performed using GRASP.  The procedure is
   represented in Figure 2.  The different steps are described next:





































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   +--------+    +--------+    +--------+
   |  fog   |    |  fog   |    |  fog   |
   | node C |    | node A |    | node B |                       +------+
   |        |    |        |    |        |                       | fog  |
   | |    | |    | |    | |    | |    | |        +------+       | mon. |
   | +----+ |    | +----+ |    | +----+ |        | NFVO |       | ctrl |
   +--------+    +--------+    +--------+        +------+       +------+
                      |             |                |              |
               (fog nodes A & B bootstrap)           |              |
                      |             |                |              |
                      |             |   periodic mcast advertisement|
                      |             |               (ID, fog_scope) |
                      |             |  <----------------------------+
                      | Mcast discovery (fog_node_ID, scope)        |
                      +-------------------------------------------->|
                      +------------>|                |              |
                      |    Mcast discovery (fog_node_ID, scope)     |
                      |             +------------------------------>|
                      |<------------+                |              |
                      |             |                |              |
                      |       Unicast advertisement (ID, fog_scope) |
                      |             |<------------------------------+
                      |<--------------------------------------------+
                      |             |                |              |
                      |    Unicast registration (ID, fog_node_ID    |
                      |             |            fog_scope, capab.) |
                      |             +------------------------------>|
                      +-------------------------------------------->|
                      |             |                |              |
               (fog nodes A & B registered)          |              |
                      |             |                |              |
   (fog node C bootstraps)          |                |              |
        |             |             |                |              |
        | Mcast discovery (fog_node_ID, scope)       |              |
        +---------------------------------------------------------->|
        +-------------------------->|                |              |
        +------------>|       Unicast advertisement (ID, fog_scope) |
        |<----------------------------------------------------------+
        |<--------------------------+                |              |
        |<------------+    Unicast registration (ID, fog_node_ID    |
        |             |             |            fog_scope, capab.) |
        +---------------------------------------------------------->|
   (fog node C registered)          |                |              |
        |             |             |                |              |

                  Figure 2: Autonomic setup of fog agents





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   o  The fog monitoring controller is regularly sending periodic
      multicast advertisement messages, which include its ID as well as
      the scope for the advertisement messages (i.e., the scope of where
      the messages have to be flooded).

      M_DISCOVERY messages are used, with new objectives and objective
      options.  GRASP specifies that "an objective option is used to
      identify objectives for the purposes of discovery, negotiation or
      synchronization".  New objective options are defined for the
      purposes of discovering potential fog agents with certain
      characteristics.  Non-limiting examples of these options are
      listed below (note that the names are just examples, and the ones
      used have to be registered by the IANA):

      *  FOGNODERADIO: used to specify a given type of radio technology,
         e.g.,: WiFi (version), D2D, LTE, 5G, Bluetooth (version), etc.

      *  FOGNODECONNECTIVITY: used to specify a given type of
         connectivity, e.g., layer-2, IPv4, IPv6.

      *  FOGNODEVIRTUALIZATION: used to specify a given type of
         virtualization supported by the node where the agent runs.
         Examples are: hypervisor (type), container, micro-kernel, bare-
         metal, etc.

      *  FOGNODEDOMAIN: used to specify the domain/owner of the node.
         This is useful to support operation of multiple domains/
         operators simultaneously on the same fog network.

      An example of discovery message using GRASP would be the following
      (in this example, the fog monitoring controller is identified by
      its IPv6 address: 2001:DB8:1111:2222:3333:4444:5555:6666):

      [M_DISCOVERY, 13948745, h'20010db8111122223333444455556666',
      ["FOGDOMAIN", F_SYNCH_bits, 2, "operator1"]]

      GRASP is used to allow the fog agents and the controller discovery
      in an autonomic way.  The extensions defined above, together with
      the use of properly scoped multicast addresses (as explained
      below), allow to precisely define which nodes participate in the
      monitoring and to gather their principal characteristics.

   o  When a fog node bootstraps, such as nodes A and B in the figure,
      they start sending multicast discovery messages within a given
      scope, that is, the intended area that composes the fog.  The
      definition of the scope depends on the scenario, and examples of
      possible scopes are:




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      *  All-resources of a given manufacturer.

      *  All-resources of a given type.

      *  All-resources of a given administrative domain.

      *  All-resources of a given user.

      *  All-resources within a topological network distance (e.g.,
         number of hops).

      *  All-resources within a geographical location.

      *  Etc.

      Combination of previous scopes are also possible.

      The discovery messages are multicast within the scope, reaching
      all the nodes that compose the specified fog resources.  This can
      be done for example using well defined IPv6 multicast addresses,
      specified for each of the different scopes.  This signaling is
      based on GRASP.  Different IPv6 multicast addresses need to be
      defined to reach each different scope, using scopes equal or
      larger than Admin-Local according to [RFC7346].

   o  In response to multicast fog discovery messages, the fog
      monitoring controller replies with unicast information messages.

   o  Fog agents can then register with a controller.  The registration
      message is unicast, and includes information on the capabilities
      of the fog node, such as:

      *  Type of node.

      *  Vendor.

      *  Energy source: battery-powered or not.

      *  Connectivity (number of network interfaces and information
         associated to them, such as radio technology type, layer-2 and
         layer-3 addresses, etc.).

      *  Etc.

      Note that registration to multiple fog monitoring controller
      instances could also be possible if a fog node wants to belong to
      several fog domains at the same time (but note that how the
      orchestration of the same resource is done by multiple



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      orchestrators is not covered by this invention).  The defined
      mechanisms support this via the use of fog IDs and FOGNODEDOMAIN
      options.

   o  A fog node C bootstraps after nodes A and B are already
      registered.  The same discovery process is followed by fog node C,
      but in addition to the regular advertisement, registration
      procedures described before, existing neighboring fog agents (such
      as A and B in this example), might also respond to discovery
      messages sent by bootstrapping nodes to provide required
      information.  This makes the procedure faster, more efficient and
      reliable.  In addition to helping the fog monitoring controller in
      the fog agent discovery process, fog agents learn themselves about
      the existence and associated capabilities of other fog agents.
      This can be used to allow autonomous monitoring by the fog agents
      without the involvement of the central controller.

4.  IANA Considerations

   TBD.

5.  Security Considerations

   TBD.

6.  Acknowledgments

   The work in this draft will be further developed and explored under
   the framework of the H2020 5G-CORAL project (Grant 761586).

7.  References

7.1.  Normative References

   [RFC2119]  Bradner, S., "Key words for use in RFCs to Indicate
              Requirement Levels", BCP 14, RFC 2119,
              DOI 10.17487/RFC2119, March 1997,
              <https://www.rfc-editor.org/info/rfc2119>.

   [RFC8174]  Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC
              2119 Key Words", BCP 14, RFC 8174, DOI 10.17487/RFC8174,
              May 2017, <https://www.rfc-editor.org/info/rfc8174>.

7.2.  Informative References

   [RFC7346]  Droms, R., "IPv6 Multicast Address Scopes", RFC 7346,
              DOI 10.17487/RFC7346, August 2014,
              <https://www.rfc-editor.org/info/rfc7346>.



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Authors' Addresses

   Carlos J. Bernardos
   Universidad Carlos III de Madrid
   Av. Universidad, 30
   Leganes, Madrid  28911
   Spain

   Phone: +34 91624 6236
   Email: cjbc@it.uc3m.es
   URI:   http://www.it.uc3m.es/cjbc/


   Alain Mourad
   InterDigital Europe

   Email: Alain.Mourad@InterDigital.com
   URI:   http://www.InterDigital.com/

































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