Usecases for Network Artificial Intelligence (NAI)
draft-zheng-opsawg-network-ai-usecases-00

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Network Working Group                                           Y. Zheng
Internet-Draft                                              China Unicom
Intended status: Informational                                     S. Xu
Expires: September 14, 2017                                     D. Dhody
                                                     Huawei Technologies
                                                          March 13, 2017

           Usecases for Network Artificial Intelligence (NAI)
               draft-zheng-opsawg-network-ai-usecases-00

Abstract

   This document discusses the scope of Network Artificial Intelligence
   (NAI), and the possible use cases that are able to demonstrate the
   advantage of applying NAI.

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Zheng, et al.          Expires September 14, 2017               [Page 1]
Internet-Draft               Usecases of NAI                  March 2017

Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
   2.  NAI Architecture  . . . . . . . . . . . . . . . . . . . . . .   3
   3.  NAI Use Cases . . . . . . . . . . . . . . . . . . . . . . . .   3
     3.1.  Traffic Predication and Re-Optimization/Adjustment  . . .   3
     3.2.  Route Monitoring and Analytics  . . . . . . . . . . . . .   4
     3.3.  Multilayer Fault Detection In NFV Framework . . . . . . .   5
     3.4.  Data Center Network Use Cases . . . . . . . . . . . . . .   7
       3.4.1.  Service Function Chaining . . . . . . . . . . . . . .   7
   4.  Contributors  . . . . . . . . . . . . . . . . . . . . . . . .   8
   5.  Security Considerations . . . . . . . . . . . . . . . . . . .   8
   6.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .   8
   7.  Acknowledgement . . . . . . . . . . . . . . . . . . . . . . .   8
   8.  References  . . . . . . . . . . . . . . . . . . . . . . . . .   8
     8.1.  Normative References  . . . . . . . . . . . . . . . . . .   9
     8.2.  Informative References  . . . . . . . . . . . . . . . . .   9
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .   9

1.  Introduction

   Current networks have become much more dynamic and complex, and pose
   new challenges for network management and optimization.  For example,
   network management/optimization should be automated to avoid human
   intervention (and thus to minimize the operational expense).
   Artificial Intelligence (AI) and Machine Learning (ML) is a promising
   approach to realize such automation, and can even do better than
   human beings.  Furthermore, the population of Software-Defined
   Networks (SDN) paradigm makes the application of Artificial
   Intelligence in networks possible, since the SDN controller has the
   complete knowledge of the network status and can control behavior of
   network nodes to implement AI decisions.

   AI and ML technologies can learn from historical data, and make
   predictions or decisions, rather than following strictly static
   program instructions.  They can dynamically adapt to a changing
   situation and enhance their own intelligence with by learning from
   new data.  It can learn and complete complicated tasks.  It also has
   potential in the network technology area especially with SDN and
   Network Function Virtualization (NFV).

   This document presents the concept of Network Artificial
   Intelligence.  It first discusses the scope of Network Artificial
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