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

[Sheng Jiang <[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?

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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