Re: [Idnet] Summary 20170814 & IDN dedicated session call for case

Dear all,

Scaling mechanisms in NFV can leverage ML features. In particular, for
instance, Reinforcement Learning could be used to tune policies scaling
target to

On Mon, Aug 14, 2017 at 11:47 AM, Haoyu song <haoyu.song@huawei.com> wrote:

> Yansen,
>
>
>
> I see two key use cases are missing in the current list: root cause
> analysis and anomaly detection. Those two are likely to use ML-based
> solutions and the first one has already received a lot of research.
>
>
>
> Haoyu
>
>
>
> *From:* IDNET [mailto:idnet-bounces@ietf.org] *On Behalf Of *yanshen
> *Sent:* Sunday, August 13, 2017 8:36 PM
> *To:* idnet@ietf.org
> *Subject:* [Idnet] Summary 20170814 & IDN dedicated session call for case
>
>
>
> Dear all,
>
>
>
> Here is a summary and some index (2017.08.14). Till now, whatever the case
> is supported or not, I tried to organize all the content and keep the core
> part. It is still welcome to contribute and discuss.
>
>
>
> If I miss something important, please let me know. Apologized in advance.
>
>
>
> Yansen
>
>
>
>
>
> ---------  Roadmap  ---------
>
> ***Aug. : Collecting the use cases (related with NM). Rough thoughts and
> requirements
>
> Sep. : Refining the cases and abstract the common elements
>
> Oct. : Deeply analysis. Especially on Data Format, control flow, or other
> key points
>
> Nov.: F2F discussions on IETF100
>
> ---------  Roadmap End  ---------
>
>
>
>
>
> 1. Gap and Requirement Analysis
>
>     1.1 Network Management requirement
>
>     1.2 TBD
>
> 2. Use Cases
>
>     2.1 Traffic Prediction
>
>                    Proposed by: yanshen@huawei.com
>
>                    Track: https://www.ietf.org/mail-
> archive/web/idnet/current/msg00131.html
>
>                    Abstract: Collect the history traffic data and external
> data which may influence the traffic. Predict the traffic in
> short/long/specific term. Avoid the congestion or risk in previously.
>
>
>
>     2.2 QoS Management
>
>                    Proposed by: yanshen@huawei.com
>
>                    Track: https://www.ietf.org/mail-
> archive/web/idnet/current/msg00131.html
>
>                    Abstract: Use multiple paths to distribute the traffic
> flows. Adjust the percentages. Avoid congestion and ensure QoS.
>
>
>
>     2.3 Application (and/or DDoS) detection
>
>                    Proposed by: aydinulas@gmx.net
>
>                    Track: https://www.ietf.org/mail-
> archive/web/idnet/current/msg00133.html
>
>                    Abstract: Detect the application (or attack) from
> network packets (HTTPS or plain) Collect the history traffic data and
> identify a service or attack (ex: Skype, Viber, DDoS attack etc.)
>
>
>
>          2.4 QoE Management
>
>                    Proposed by: albert.cabellos@gmail.com
>
>                    Track: https://www.ietf.org/mail-
> archive/web/idnet/current/msg00137.html
>
>                    Abstract: Collect low-level metrics (SNR, latency,
> jitter, losses, etc) and measure QoE. Then use ML to understand what is the
> relation between satisfactory QoE and the low-level metrics. As an example
> learn that when delay>N then QoE is degraded, but when M<delay<N then QoE
> is satisfactory for the customers (please note that QoE cannot be measured
> directly over your network). This is useful to understand how the network
> must be operated to provide satisfactory QoE.
>
>
>
>          2.5 (Encrypted) Traffic Classification
>
>                    Proposed by: jerome.francois@inria.fr;
> mskim16@etri.re.kr
>
>                    Track: [Jerome] https://www.ietf.org/mail-
> archive/web/idnet/current/msg00141.html ; [Min-Suk Kim]
> https://www.ietf.org/mail-archive/web/idnet/current/msg00153.html
>
>                    Abstract:
>
>                             [Jerome] collect flow-level traffic metrics
> such as protocol information but also meta metrics such as distribution of
> packet sizes, inter-arrival times... Then use such information to label the
> traffic with the underlying application assuming that the granularity of
> classification may vary (type of application, exact application name,
> version...)
>
>                             [Min-Suk Kim] continuously collect packet
> data, then applying learning process for traffic classification with
> generating application using deep learning models such as CNN
> (convolutional neural network) and RNN (recurrent neural network). Data-set
> to apply into the models are generated by processing with features of
> information from flow in packet data.
>
>
>
>          2.6 TBD
>
>
>
> 3. Data Focus
>
>     3.1 Data attribute
>
>     3.2 Data format
>
>     3.3 TBD
>
>
>
> 4. Support Technologies
>
>     4.1 Benchmarking Framework
>
>                    Proposed by: pedro@nict.go.jp
>
>                    Track: https://www.ietf.org/mail-
> archive/web/idnet/current/msg00146.html
>
>                    Abstract: A proper benchmarking framework comprises a
> set of reference procedures, methods, and models that can (or better
> *must*) be followed to assess the quality of an AI mechanism proposed to be
> applied to the network management/control area. Moreover, and much more
> specific to the IDNET topics, is the inclusion, dependency, or just the
> general relation of a standard format enforced to the data that is used
> (input) and produced (output) by the framework, so a kind of "data market"
> can arise without requiring to transform the data. The initial scope of
> input/output data would be the datasets, but also the new knowledge items
> that are stated as a result of applying the benchmarking procedures defined
> by the framework, which can be collected together to build a database of
> benchmark results, or just contrasted with other existing entries in the
> database to know the position of the solution just evaluated. This
> increases the usefulness of IDNET.
>
>
>
>     4.2 TBD
>
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>
>

-- 
*Oscar Mauricio Caicedo Rendón*
*PhD Computer Science - Federal University of Rio Grande do Sul*
*Full Profesor - University of Cauca*

-- 

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