Re: [Nmlrg] Using Machine Learning for Network Device Initial Configurations-//RE: Machine Learning in network - solicitation for use cases

["Liubing (Leo)" <[e-mail]>]

> >[Bing] The most important precondition is the training data. The
> >training data should contain a decent number of network samples. The
> >minimum number depends on the quality of the data.
> 
> What does you mean the "quality" of the data here? Is there any general
> standards to qualify or measure the "quality" of the data?

[Bing] There are some dimensions (afaik) to judge the quality of the data:

A. The data should contain the finable knowledge. 
Incomplete or chaotic data is useless for the program. (This is obvious.)

B. The content of the training data should be consistent with the target scenarios. 
One specific database cannot cover all scenarios. So the quality is highly target scenario related. 

C. The data should represent the most usual/typical cases rather than corner cases. 
However, this is not saying the corner case data is NOT needed. It is valuable, but it should NOT be the main objects of the database.

D. Diversity of the data.
For example, for a specific type of network (e.g. DC networks, IPRAN, fixed access, core etc.) , if the data covers different scalability, different designs of that specific type, it is good quality.

E. Additionally, it might be better that the data are from real operational networks.

If the data fits all of the dimensions, maybe a small number of samples could be sufficient for the program to learn the expected patterns.

B.R.
Bing

> Regards,
> 
> Sheng
> 
> >And each network sample should contain
> >complete topology information, network planning manifest and every
> >device's information and complete configuration lines.
> >
> >For constrains, in general there are two:
> >1) As you see, this is actually a Data-Mining process, which strongly
> >consumes computing resources and time, especially when the training data
> is huge.
> >2) The results are only prediction, they are NOT guaranteed to be
> >valid, and currently we lack a mechanism to evaluate them instantly.
> >
> >B.R.
> >Bing
> >
> >
> >> Best regards,
> >>
> >> Sheng
> >>
> >> >This is to leverage the automation on network initial configuration
> >> >and only require a minimal set of input such as high-level network
> >> >planning, which includes architecture design, address pool/block
> >> >assignment, protocol selection etc.
> >> >
> >> >The advantages of using Machine Learning on network initial
> >> >configuration could possibly be:
> >> >- Saving human cost
> >> >- Flexibility
> >> >  - dynamically generating configurations on site
> >> >  - adaptive to different types of networks
> >> >- The ability to continuously optimize the parameters, if the
> >> >program also learning the running performance
> >> >- etc.
> >> >
> >> >Comments are welcomed.
> >> >
> >> >Best regards,
> >> >Bing
> >> >
> >> >
> >> >> -----Original Message-----
> >> >> From: nmlrg [mailto:[e-mail]] On Behalf Of Sheng
> >> >> Jiang
> >> >> Sent: Monday, August 31, 2015 11:16 AM
> >> >> To: [e-mail]
> >> >> Subject: [Nmlrg] Machine Learning in network - solicitation for
> >> >> use cases
> >> >>
> >> >> Hi, all,
> >> >>
> >> >> Thanks for subscribe to NMLRG (Network Machine Learning) mail list.
> >> >> As we know, there are already many ongoing researches for Machine
> >> >> Learning in network, in many areas. But up to now, there are few
> >> >> matured applications yet. So it is the time for a Research Group
> >> >> to work on this future-oriented technology.
> >> >>
> >> >> The first step would be to collect possible use cases: where the
> >> >> machine learning mechanism could be used in networks. The use
> >> >> cases does not need to be mature, but should have potential.
> >> >>
> >> >> Note that this topic is rapidly moving from academic research into
> >> >> practical application. Therefore, use cases from university
> >> >> environments, industrial research and development organizations
> >> >> are all
> >> welcome.
> >> >>
> >> >> Best regards,
> >> >>
> >> >> Sheng
> >> >> _______________________________________________
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> >> >> [e-mail]
> >> >> https://www.irtf.org/mailman/listinfo/nmlrg
> >> >
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