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

[yanshen <[e-mail]>]

Thank you David.

Really good suggestion for planning a new blue map.

I think it is just the significant to speak out all things possible.  The introspection after advancing will be always valuable.

Yansen

发件人: David Meyer [mailto:[e-mail]]
发送时间: 2017年5月10日 20:34
收件人: yanshen <[e-mail]>
抄送: Ing-Jyh Tsang <[e-mail]>; [e-mail]
主题: Re: [Idnet] 答复: Benefits of Introducing AI into Network

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 <[e-mail]<mailto:[e-mail]>> 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.
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发件人: IDNET [mailto:[e-mail]<mailto:[e-mail]>] 代表 Ing-Jyh Tsang
发送时间: 2017年5月10日 14:24
收件人: Sheng Jiang <[e-mail]<mailto:[e-mail]>>; [e-mail]<mailto:[e-mail]>
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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