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Network Working Group                                              Z. Li
Internet-Draft                                                  J. Zhang
Intended status: Informational                       Huawei Technologies
Expires: May 4, 2017                                    October 31, 2016


        An Architecture of Network Artificial Intelligence(NAI)
                   draft-li-rtgwg-network-ai-arch-00

Abstract

   Artificial intelligence is an important technical trend in the
   industry.  With the development of network, it is necessary to
   introduce artificial intelligence technology to achieve self-
   adjustment, self- optimization, self-recovery of the network through
   collection of huge data of network state and machine learning.  This
   draft defines the architecture of Network Artificial Intelligence
   (NAI), including the key components and the key protocol extension
   requirements.

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 inRFC 2119 [RFC2119]

Status of This Memo

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

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

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

   This Internet-Draft will expire on May 4, 2017.

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.  Code Components extracted from this document must
   include Simplified BSD License text as described in Section 4.e of
   the Trust Legal Provisions and are provided without warranty as
   described in the Simplified BSD License.

Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
   2.  Terminology . . . . . . . . . . . . . . . . . . . . . . . . .   3
   3.  Architecture  . . . . . . . . . . . . . . . . . . . . . . . .   3
     3.1.  Reference Model . . . . . . . . . . . . . . . . . . . . .   3
     3.2.  Requirement of Protocol Extensions  . . . . . . . . . . .   4
   4.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .   5
   5.  Security Considerations . . . . . . . . . . . . . . . . . . .   5
   6.  Normative References  . . . . . . . . . . . . . . . . . . . .   5
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .   5

1.  Introduction

   Artificial Intelligence is an important technical trend in the
   industry.  The two key aspects of Artificial Intelligence are
   perception and cognition.  Artificial Intelligence has evolved from
   an early non-learning expert system to a learning-capable machine
   learning era.  In recent years, the rapid development of the deep
   learning branch based on the neural network and the maturity of the
   big data technology and software distributed architecture make the
   Artificial Intelligence in many fields (such as transportation,
   medical treatment, education, etc.) have been applied.  With the
   development of network, it is necessary to introduce artificial
   intelligence technology to achieve self-adjustment, self-
   optimization, self-recovery of the network through collection of huge
   data of network state and machine learning.  The areas of machine
   learning which are easier to be used in the network field may
   include: troubleshooting of network problems, network traffic
   prediction, traffic optimization adjustment, security defense,
   security auditing, etc., to implement network perception and
   cognition.

   This draft defines the architecture of Network Artificial
   Intelligence (NAI), including the key components and the key protocol
   extension requirements.





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2.  Terminology

   AI: Artificial Intelligence

   NAI: Network Artificial Intelligence

3.  Architecture

3.1.  Reference Model

   +------------------------------+    +------------------------------+
   |           Domain 1           |    |          Domain 2            |
   |        +------------+        |    |        +------------+        |
   |        |  Central   |        |    |        |  Central   |        |
   |        | Controller |----------------------| Controller |        |
   |        |            |        |    |        |            |        |
   |        |            |        |    |        |            |        |
   |        +------------+        |    |        +------------+        |
   |         /          \         |    |         /          \         |
   |        /            \        |    |        /            \        |
   |       /              \       |    |       /              \       |
   | +--------+        +--------+ |    | +--------+        +--------+ |
   | |        |        |        | |    | |        |        |        | |
   | |Network | ...... |Network | |    | |Network | ...... |Network | |
   | | Device |        | Device | |    | | Device |        | Device | |
   | |    1   |        |    N   | |    | |    1   |        |    N   | |
   | +--------+        +--------+ |    | +--------+        +--------+ |
   |                              |    |                              |
   +------------------------------+    +------------------------------+

    Figure 1: An Architecture of Network Artificial Intelligence(NAI)

   The architecture of Network artificial intelligence includes
   following key component:

   1.  Central Controller: Centralized controller is the core component
   of Network Artificial Intelligence which can be called as 'Network
   Brain'.  It man collect huge data of network states, store the data
   based on the big data platform, and carry on the machine learning, to
   achieve network perception and cognition, including network self-
   optimization, self- adjustment, self-recovery, intelligent fault
   location and a series of network artificial intelligence goals.

   2.  Network Device: IP network operation and maintenance are always a
   big challenge since the network can only provide limited state
   information.  The network states includes but are not limited to
   topology, traffic engineering, operation and maintenance information,
   network failure information and related information to locate the



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   network failure.. In order to provide these information, the network
   must be able to support more OAM mechanisms to acquire more state
   information and report to the controller.  Then the controller can
   get the complete state information of the network which is the base
   of Network Artificial Intelligence(NAI).

   3.  Southbound Protocol and Models of Controller: As network devices
   provide huge network state information, it proposes a number of new
   requirements for protocols and models between controllers and network
   devices.  The traditional southbound protocol such as Netconf and
   SNMP can not meet the performance requirements.  It is necessary to
   introduce some new high-performance protocols to collect network
   state data.  At the same time, the models of network data should be
   completed.  Moreover with the introduction of new OAM mechanisms of
   network devices, new models of network data should be introduced.

   4.  Northbound Model of Controller: The goal of the Network
   Artificial Intelligence is to reduce the technical requirements on
   the network administrators and release them from the heavy network
   management, control, maintenance work.  The abstract northbound model
   of the controller for different network services should be simple and
   easy to be understood.

3.2.  Requirement of Protocol Extensions

   REQ 01: The new southbound protocol of the controller should be
   introduced to meet the performance requirements of collecting huge
   data of network states.

   REQ 02: The models of network elements should be completed to collect
   the network states based on the new southbound protocol of the
   controller.

   REQ 03: New OAM mechanisms should be introduced for the network
   devices in order to acquire more types of network state data.

   REQ 04: New models of network elements should be introduced as the
   new OAM mechanisms are introduced.

   REQ 05: The operation models of network elements should be completed
   based on the new southbound protocol to carry on the corresponding
   network operation as the result of Network Artificial Intelligence.

   REQ 06: The abstract network-based service models should be provided
   by the controller as the northbound models to satisfy the
   requirements of different services.





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4.  IANA Considerations

   This document makes no request of IANA.

5.  Security Considerations

   TBD.

6.  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,
              <http://www.rfc-editor.org/info/rfc2119>.

Authors' Addresses

   Zhenbin Li
   Huawei Technologies
   Huawei Bld., No.156 Beiqing Rd.
   Beijing  100095
   China

   Email: lizhenbin@huawei.com


   Jinhui Zhang
   Huawei Technologies
   Huawei Bld., No.156 Beiqing Rd.
   Beijing  100095
   China

   Email: jason.zhangjinhui@huawei.com


















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