Re: [Idnet] Intelligence-Defined Network Architecture and Call for Interests

Hi Brian,
    You are right.
     I have worked in an institute more than five years. The main work is  use ML to solve the domain problem.  However, for privacy reason, it's very hard to get some real domain data sets. So the results learned by any ML models is not reliable.
    So I think the important/first thing we do is to construct real and reliable data sets of network domain. Just like UCI repository (https://archive.ics.uci.edu/ml/datasets.html) or images datasets (https://www.cs.utah.edu/~lifeifei/datasets.html) . Only the common and real data sets are agreed with all we, different ML models can be applied to validate and predict. 
   So my point is data set, not ML model.  We can select some existing models (SVM, ELM, bayes, etc) to learn different tasks. We do not research the principle of ML algorithm, but use them to slove network problems.

 Best regards,

Xiaojian

-----邮件原件-----
发件人: IDNET [mailto:idnet-bounces@ietf.org] 代表 Brian E Carpenter
发送时间: 2017年3月30日 23:37
收件人: Sheng Jiang <jiangsheng@huawei.com>; David Meyer <dmm@1-4-5.net>
抄送: idnet@ietf.org
主题: Re: [Idnet] Intelligence-Defined Network Architecture and Call for Interests

Agreed, and there are (still) two key points:

1. What is our underlying model (what Dave called a "theory of networking")? With no such model, it's very hard to tell the ML system what to do.

2. And as others have said: get hold of large datasets that can processed by ML according to that model. For developing open solutions, a corpus of open data sets seems essential. As anybody from the network measurement community will tell you, getting hold of large data sets from operators is extremely difficult for both privacy and commercial reasons.

   Brian

On 31/03/2017 03:41, Sheng Jiang wrote:
> Hi, David,
> 
> I think I agree with you, but in slight different  expression. Yes, the hard parts of getting ML into Network lies on machine learning. But, it is not that we need to develop any new ML technical/algorithms for networking in particular. It is that we MUST re-set up our network domain knowledge from the perspective of applying ML. My slides [0] does not suggest that *someone else* will handle the ML part. Actually, oppositely, it suggests some experts who have knowledge of both ML and network (probably we) would develop tools/algorithms/systems to handle the ML part for other network experts (more than 98 percent of current network administrators). So that, these network experts would be allowed to manage their network easily with intelligence association, but no need to become ML experts themselves. Here, we would like to treat the network administrators like the users in other successful ML application. We are the domian experts to do the dirty AI work for them.
> 
> I believe we have common understanding in the above description. But certainly my slides needs further refine to clarify my viewpoint.
> 
> Best regards,
> 
> Sheng
> ________________________________
> From: IDNET [idnet-bounces@ietf.org] on behalf of David Meyer 
> [dmm@1-4-5.net]
> Sent: 29 March 2017 2:01
> To: Sheng Jiang
> Cc: idnet@ietf.org
> Subject: Re: [Idnet] Intelligence-Defined Network Architecture and 
> Call for Interests
> 
> s/NMRL/NMLRG/   (sorry about that). Dave
> 
> On Tue, Mar 28, 2017 at 10:59 AM, David Meyer <dmm@1-4-5.net<mailto:dmm@1-4-5.net>> wrote:
> Hey Sheng,
> 
> I just wanted to revive my key concern on [0] (same one I made at the NMRL): The hard parts of getting Machine Learning intelligence into Networking is the Machine Learning part. In addition, successful deployment of ML requires knowledge of ML combined with domain knowledge. We definitely have the domain knowledge; the problem is that we don't have the ML knowledge, and this is one of the big factors holding us back; see e.g. Andrew's discussion of talent in [1].  Slides such as [0] seem to imply that *someone else* (in particular, not us)  will handle the ML part of all of this. I'll just note that in general successful deployments of ML don't work this way; the domain experts will have to learn ML (and vice versa) for us to be successful (again, see [1] and many others).
> 
> Perhaps a useful exercise would be to write an ID that makes your assumptions explicit?
> 
> Thanks,
> 
> Dave
> 
> 
> [0] 
> https://www.ietf.org/proceedings/97/slides/slides-97-nmlrg-intelligenc
> e-defined-network-01.pdf [1] 
> https://hbr.org/2016/11/what-artificial-intelligence-can-and-cant-do-r
> ight-now
> 
> 
> On Tue, Mar 28, 2017 at 9:29 AM, Sheng Jiang <jiangsheng@huawei.com<mailto:jiangsheng@huawei.com>> wrote:
> Hi, all,
> 
> Although there are many understanding for Intelligence-Defined Network, we are actually using this IDN as a term reference to the SDN-beyond architecture that we presented in IETF97, see the below link. A reference model is presented in page 3, while potential standardization works is presented in page 9.
> 
> https://www.ietf.org/proceedings/97/slides/slides-97-nmlrg-intelligenc
> e-defined-network-01.pdf
> 
> Although it might be a little bit too early for AI/ML in network giving the recent story of the concluded proposed NMLRG, we still would like to call for interests in IDN. Anybody (on site in Chicago this week) are interested in this or even wider topics regarding to AI/ML in network, please contact me on jiangsheng@huawei.com<mailto:jiangsheng@huawei.com> . Then we may have an informal meeting to discuss some common interests and potential future activities (not any activities in IETF, but also other STO or experimental trails, etc.)  on Thursday morning.
> 
> FYI, we have already working on a Work Item, called IDN in the ETSI NGP (Next Generation Protocol) ISG, links below.
> 
> https://portal.etsi.org/tb.aspx?tbid=844&SubTB=844
> https://portal.etsi.org/webapp/WorkProgram/Report_WorkItem.asp?WKI_ID=
> 51011
> 
> Meanwhile, please do use this mail list as a forum to discuss any topics that may applying AI/ML into network area.
> 
> Best regards,
> 
> Sheng
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