Re: [Idnet] Benefits of Introducing AI into Network

[Javier Antich Romaguera <[e-mail]>]

>>Let's take a specific example: the CASM work in the IETF. One approach to massive scale support of address and prefix management is a massive centralised database and an old-fashioned (NETCONF/YANG style) mechanism to push allocations out into the network. Another approach is a large set of distributed autonomic prefix managers that request new space from a distributed address pool when necessary. The second one is definitely a target for machine learning, in order to set optimal parameters for the individual prefix managers.

I´m sorry, but what is the relationship between something being centralized or distributed and using or not a mechanism like Netconf using YANG models to communicate certain information (in this case prefixes or addresses)? They are entirely orthogonal. Wherever you do your processing (centralized or distributed) or your storage (centralized or distributed) you still need a mechanism to model your data (YANG or other) and a mechanism to encode and transport your data (Netconf or other). Not sure what exactly is old-fashioned on Netconf/YANG for this purposes discussion, and what is has to do with using AI or not.

Thanks
Javier.
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Javier Antich Romaguera
Product Line Manager. JUNOS Team.
Juniper Networks
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“Achieving the difficult, trying the impossible"


From: IDNET <[e-mail]<mailto:[e-mail]>> on behalf of Brian E Carpenter <[e-mail]<mailto:[e-mail]>>
Organization: University of Auckland
Date: Thursday, May 11, 2017 at 3:35 AM
To: "Fang, Luyuan" <[e-mail]<mailto:[e-mail]>>, Sheng Jiang <[e-mail]<mailto:[e-mail]>>, "[e-mail]<mailto:[e-mail]>" <[e-mail]<mailto:[e-mail]>>
Subject: Re: [Idnet] Benefits of Introducing AI into Network

On 10/05/2017 18:26, Fang, Luyuan wrote:
I think what is driving all these points is scale. Without intelligence, very large scale networks simply become unmanageable,

I am not sure that statement is always true. It is safe to say that without *distributed solutions*, very large scale networks become unmanageable, and AI technologies can be used to support distributed solutions.

Let's take a specific example: the CASM work in the IETF. One approach to massive scale support of address and prefix management is a massive centralised database and an old-fashioned (NETCONF/YANG style) mechanism to push allocations out into the network. Another approach is a large set of distributed autonomic prefix managers that request new space from a distributed address pool when necessary. The second one is definitely a target for machine learning, in order to set optimal parameters for the individual prefix managers.

and cannot maintain service availability. Just adding work force would not help.

It will help where the problem can genuinely be tackled by "divide and conquer", for example where localised optimisation and localised repair can help. But when localised solutions are inadequate and traditional centralised solutions do not scale, AI can be the answer.

I would suggest to add
-          ability to manage network at scale (as 2nd point)
-          potential self-healing (after “predictive”)

Agreed.

    Brian
Thx,
Luyuan
From: IDNET <[e-mail]<mailto:[e-mail]>> on behalf of Sheng Jiang <[e-mail]<mailto:[e-mail]>>
Date: Tuesday, May 9, 2017 at 7:53 PM
To: "[e-mail]<mailto:[e-mail]>" <[e-mail]<mailto:[e-mail]>>
Subject: [Idnet] Benefits of Introducing AI into Network
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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