Re: [Idnet] IETF99 for applying AI/ML into network management: Follow-up

Simone Ferlin <> Thu, 27 July 2017 03:06 UTC

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From: Simone Ferlin <>
Date: Thu, 27 Jul 2017 12:04:58 +0900
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To: Pedro Martinez-Julia <>
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Subject: Re: [Idnet] IETF99 for applying AI/ML into network management: Follow-up
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Hello everyone,

I have been following this list for some time, but I have not been in
the meetings (f2f nor online) yet due to lack of time.
Do you have a summary of these meetings, including the topics
discussed at IETF'99, so that people like me could engage in the
discussions? I see you started a thread here, but not with much I
could understand the starting point.

Thanks and cheers,

On Thu, Jul 27, 2017 at 11:16 AM, Pedro Martinez-Julia <> wrote:
> Dear Yanshen,
> Thanks for your comments, please find my reply in-line.
> On Thu, Jul 27, 2017 at 01:54:58AM +0000, yanshen wrote:
>> > For me that depends on the algorithmic representation. We have two types of
>> > variables:
>> >
>> > - Measurable and controllable variables, such as the load of a resource
>> >   managed by the solution, which can be known and can be altered by some
>> >   decision of such solution (increase/decrease the amount of resources).
>> >
>> > - Measurable but non-controllable, which are outside the control of the
>> >   solution. They can be known and used to take decisions but cannot be
>> >   altered (directly or indirectly) by the solution. A simple example can
>> >   be found in the occurrence of some event that can affect to a system,
>> >   such as the number of attendees to a baseball match. The management
>> >   solution cannot control such variable but uses it to determine the
>> >   amount of network resources assigned to the network of the stadium.
>> I have a similar thought. I try to divide the variables into objective
>> and subjective (may corresponding to your controllable and
>> non-controllable. Perhaps a little different).
>>       - The objective data means that we can capture, input or measure it
>>       periodically from any source (whatever it is). This class of data
>>       needs to be focused and formatted, including the context and format
>>       and so on. One of the cases is QoS value which I just mentioned.
>>       - The subjective data means that it is imported into the machine
>>       (AI/Brain/Knowledge system) temporarily or irregularly. This class
>>       of data may be high-level and diversity. The solution for them I
>>       think should be pushed to application layer. It is not our dishes.
>>       Another thought is that we should try to obtain more subjective data
>>       by objective way, for example, we should try to change the way of
>>       obtaining the "reward" feedback from "user randomly report" to
>>       "periodically capture". Unless such, it should not be included into
>>       our consideration.
>> I think it is significant to combine our two dimensions into one. Of
>> course other dimensions may also considerable.
> They are different aspects and, of course, they must be combined. My
> classification is based on the "control theory", which clearly states
> what a controlled variable means and the strict definition of the loop
> closure (closed-loop controller), which implies to check such variables
> and confirm they are changed according to the intended objective after
> some change/s is/are applied to the controlled environment.
> On the other hand, your view is more practical in terms of the scope of
> the information in relation to the AI algorithm and its needs. It would
> be good to simplify/unify both dimensions but also to simplify/unify the
> differences within them. I mean that it would be probably a good option
> to consider any kind of data homogeneously. I am using a simple ontology
> to resolve such problem, so we could probably use it and a corresponding
> YANG model, as I proposed in my other message, to achieve such unified
> view. Please, let me know your thoughts. Thank you very much.
> Regards,
> Pedro
> --
> Pedro Martinez-Julia
> Network Science and Convergence Device Technology Laboratory
> Network System Research Institute
> National Institute of Information and Communications Technology (NICT)
> 4-2-1, Nukui-Kitamachi, Koganei, Tokyo 184-8795, Japan
> Email:
> ---------------------------------------------------------
> *** Entia non sunt multiplicanda praeter necessitatem ***
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