Re: [Idnet] 答复: Benefits of Introducing AI into Network

David Meyer <dmm@1-4-5.net> Wed, 10 May 2017 12:34 UTC

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From: David Meyer <dmm@1-4-5.net>
Date: Wed, 10 May 2017 05:34:20 -0700
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To: yanshen <yanshen@huawei.com>
Cc: Ing-Jyh Tsang <ing-jyh.tsang@nokia-bell-labs.com>, "idnet@ietf.org" <idnet@ietf.org>
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Subject: Re: [Idnet] =?utf-8?b?562U5aSNOiBCZW5lZml0cyBvZiBJbnRyb2R1Y2luZyBB?= =?utf-8?q?I_into_Network?=
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Folks,

I would encourage people to ask the question: How does <this> actually
work?

where <this> are claims being made about AI and what it might do. If we
can't answer this basic question then we're off on the wrong foot. We need
to understand the technical details of how different techniques work in
order to be successful. This has been proven out time and time again in
both the research community and in industry.  More specifically, falling
prey to the ML hype machine won't serve anyone's purposes.

So when we make wish-lists about what ML might do for us, let's remember to
ask the important questions, such as: what data sets to we have (and what
are their properties), what assumptions are we making, what models are
appropriate, and what are their computation properties (both in training
and inference), to name a few. For example, in the case of something as
simple as PCA (or if you like single hidden layer linear auto-encoder), we
assume linearity, that mean and variance are sufficient statistics, that
large variances have important structure, and that the principle components
(rows of P) are orthogonal. All, some or none of these may be true for a
given network data set.

BTW, even the term AI is being used in inconsistent ways. For the most part
it seems like people are talking about Machine Learning (weak or narrow AI)
as opposed to something like AGI (strong AI). While the problems are
related (weak vs. strong AI, an AGI might use ML techniques), they are in
very different stages of development and have very different properties.

It will serve us well to be precise about what we're talking about.

Dave


On Wed, May 10, 2017 at 5:04 AM, yanshen <yanshen@huawei.com> wrote:

> Dear Ing-Jyh Tsang and all,
>
>
>
> There is some personal thought of these topics. Comments and suggestions
> are most welcome!
>
>
>
> --Towards Fully Autonomic Network
>
>
>
>         The advantage (or say aim) of a fully autonomic network is
> implementing the close-ring of “Sensing-Analysis-Decision”, which will
> minimize the input of manual labor in the pure operating actions (without
> analysis). In current, the process of sensing, analysis and decision are
> independent to each other and the cooperation of these is depend on manual
> work. The shortage is caused by the lack of analysis ability of network,
> which is exactly AI technology is good at.
>
>
>
> --Ability of Handling Complex Issues
>
>
>
>         The machine learning (ML) method is one of the best way to solve
> the complex problems, such as classifying problems and optimal decision
> problems. In network area, the problems that related to resource management
> and route decision are typically complex. The introducing of AI method
> essentially build up a centralized analysis system which may provide a
> global view solution for the network, which cannot (or difficult) be
> implemented with the current highly distributed architecture. A centralized
> architecture may not solve all problems but it is indeed a good supplement
> for nowadays network.
>
>
>
> --More Adaptive and Flex
>
>
>
>        The ML model can output different policies according to the input
> training data. Each domain of network may own different characters (e.g.
> different traffic character). It is possible to provide adaptive solution
> for different situation via uniform train model.
>
>
>
> --Predictive
>
>
>
>         Prediction is one of the most important attribute that AI
> technology brings. On one hand, the prediction (e.g. traffic prediction or
> failure prediction) that produced by AI algorithm can make the network
> manager preventing the risky failure and nip the problem in the bud. It
> will reduce the risk of network fault so that the cost of recovering from
> the failure. On the other hand, the prediction can be also used to evaluate
> the reliability of network policy. For example, to evaluate what is the
> probability of a VPN route is faced with congestion.
>
>
>
> --Potential Self-Evolving Ability
>
>
>
>         The ML method is self-evolving, which may provide same attribute
> to network. According to training the network decision model continuously,
> the network can match the change of service and traffic. Actually, the
> optimization is just the process that modifying the network parameters to
> match the requirement. The self-evolving feature of ML method may bring the
> same ability to the network.
>
>
>
> --Potential Decision Efficiency
>
>
>
>         The machine learning algorithm can solve the decision problems
> which has been verified in other area (e.g. AlphaGo). It will be valuable
> to train a decision model of network that input the current state and
> output corresponding policies (e.g. device configuration parameters). This
> model may configure the network device directly when necessary or provide
> probable configurations as choices when doubt. Both of them will save the
> time and cost and make the decision process toward efficient.
>
>
>
> Yansen
>
> Huawei Technologies Co., Ltd.
>
> [image: Company_logo]
>
> Cellphone :+86-13488809882 <+86%20134%208880%209882>
> Email :yanshen@huawei.com
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> *发件人:* IDNET [mailto:idnet-bounces@ietf.org] *代表 *Ing-Jyh Tsang
> *发送时间:* 2017年5月10日 14:24
> *收件人:* Sheng Jiang <jiangsheng@huawei.com>om>; idnet@ietf.org
> *主题:* Re: [Idnet] Benefits of Introducing AI into Network
>
>
>
> Is it possible to give a brief (or detail) description of each o the
> topic?
>
> On 10/05/2017 04:53, Sheng Jiang wrote:
>
> In ETSI NGP ISP, we have an Work Item for IDN (Intelligence-Defined
> Network). In one of the general sections, We briefly describes the benefits
> of introducing AI into network, as below.
>
>
>
> -          Towards Fully Autonomic Network
>
> -          Ability of Handling Complex Issues
>
> -          More Adaptive and Flex
>
> -          Predictive
>
> -          Potential Self-Evolving Ability
>
> -          Potential Decision Efficiency
>
>
>
> If there are major benefits that we have not covered, we would like to
> include.
>
>
>
> Regards,
>
>
>
> Sheng
>
>
>
>
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