Internet DRAFT - draft-yan-idn-consideration
draft-yan-idn-consideration
none S. Yan
Internet-Draft Huawei
Intended status: Informational P. Martinez-Julia
Expires: May 3, 2018 NICT/Japan
A. Cabellos-Aparicio
Technical University of Catalonia
October 30, 2017
A General Considerations of Intelligence Driven Network
draft-yan-idn-consideration-00
Abstract
This document aims to pinpoint the work scope of Intelligence Driven
Network (IDN) and mine the potential standardization work. Firstly,
the problems and new requirements for the existing methods are
analyzed. Numbers of high value use-cases are proposed as examples
to instantiate them. A benchmark framework design is proposed, which
is important during the machine learning and inference process.
Finally, a reference model of IDN is proposed, based on which the
potential standardization work is analyzed.
Requirements Language
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
"SHOULD", "SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED", "MAY", and
"OPTIONAL" in this document are to be interpreted as described in
[RFC2119] when they appear in ALL CAPS. When these words are not in
ALL CAPS (such as "should" or "Should"), they have their usual
English meanings, and are not to be interpreted as [RFC2119] key
words.
Status of This Memo
This Internet-Draft is submitted in full conformance with the
provisions of BCP 78 and BCP 79.
Internet-Drafts are working documents of the Internet Engineering
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material or to cite them other than as "work in progress."
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This Internet-Draft will expire on May 3, 2018.
Copyright Notice
Copyright (c) 2017 IETF Trust and the persons identified as the
document authors. All rights reserved.
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described in the Simplified BSD License.
Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 3
2. Scope and use cases . . . . . . . . . . . . . . . . . . . . . 4
2.1. Scope . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.2. High Value Use Cases . . . . . . . . . . . . . . . . . . 4
2.2.1. Traffic Prediction . . . . . . . . . . . . . . . . . 4
2.2.2. QoS management . . . . . . . . . . . . . . . . . . . 5
2.2.3. Deep Reinforcement-Learning Control of the Network . 6
2.2.4. QoE Management via Supervised Learning . . . . . . . 9
2.2.5. TBD . . . . . . . . . . . . . . . . . . . . . . . . . 10
3. Measurement and Data Format . . . . . . . . . . . . . . . . . 10
3.1. Measurement Tools and Methods . . . . . . . . . . . . . . 10
3.2. Data Format Analysis . . . . . . . . . . . . . . . . . . 10
4. Benchmarking Framework . . . . . . . . . . . . . . . . . . . 11
5. References Model and Potential Standardization Points . . . . 12
5.1. References Model . . . . . . . . . . . . . . . . . . . . 12
5.2. Measurement . . . . . . . . . . . . . . . . . . . . . . . 15
5.3. Data representation, transport and aggregation . . . . . 15
5.4. Legacy Device Route control . . . . . . . . . . . . . . . 16
5.5. TBD . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
6. Security Considerations . . . . . . . . . . . . . . . . . . . 16
7. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 16
8. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . 16
9. References . . . . . . . . . . . . . . . . . . . . . . . . . 16
9.1. Normative References . . . . . . . . . . . . . . . . . . 16
9.2. Informative References . . . . . . . . . . . . . . . . . 17
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 18
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1. Introduction
Recently, AI technology has made a great achievement and become more
and more popular. The combination of AI and network is also a hot
topic. The concept of Intelligence Driven Network (IDN) has been
proposed. This concept is intended to describe the schemes that
introducing AI into network and provide new solutions for the current
and future network problems. There has been quite a lot of
discussions about the AI application in the network in both academic
and industrial area. However, the detail works, especially the
potential standard points are still not clear.
In this document, we want to summerize the valuable content in the
idnet maillist and make clear about the following.
o What are the requirements? In network area, what problems need AI
to solve? It always makes misunderstanding that AI is almighty.
But it is factual that AI has both advantages and disadvantages.
The work scope and scenarios, which AI may be useful and perform
well, will be discussed and analyzed.
o What are the gap when combining AI and network? The modern AI
algorithms are proposed by image processing area but not network.
Most of the algorithms cannot be migrated and used directly. Take
the data format as an example. The input and output of the AI
algorithm may be just numerical matrix or vector. The network
data are not entirely formatted and regular. They need to be
translated or converted before and after the algorithm. The gaps,
like the data format, data orchestration and etc., will be
analyzed.
o What are the potential and new standard points? The intruduction
of AI will bring new requirements for the current network. For
example, the AI engine may need high frequency and high accuracy
data to feed. Moreover, these data needs to be captured and
transmitted in real-time and continuously. What improvements
should be accomplished for the existing protocols? Whether there
are new protocol requirements? What communication processes are
universal and what kinds of data format that can be utilized in
most of the scenarios?
This document aims to become the blueprint for the future work. The
structure is organized as following. Section 2 describes the work
scope of idnet and summerize the use cases. Section 3 indicates the
analysis of measurement and data format. Section 4 discusses about
the benchmark of data. Section 5 abstracts the IDN architecture and
gives a brief analysis of potential standard points. Section 6
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points out the new security challenge which AI brings to the network.
Section 7 to 9 are IANA, Acknowledgements and References.
TBD
2. Scope and use cases
TBD
2.1. Scope
A general description about what should be focused during the IETF
work and what should not. Clarify the work boundary. TBD
2.2. High Value Use Cases
There are numbers of use cases, which have been discussed in the
idnet mail list. Describe the scenarios that may be useful and
valuable. A details analysis may be helpful for the data and
protocol design.
2.2.1. Traffic Prediction
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.
The process, data format and message needs are:
Process: 1. Data collection (e.g. traffic sample of physical/logical
port ); 2. Training Model; 3. Real-time data capture and input; 4.
Predication output; 5. Fix error and go back to 3.
Data Format:
Time : [Start, End, Unit, Number of Value, Sampling Period]
Position: [Device ID, Port ID]
Direction: IN / OUT
Route : [R1, R2, ..., RN] (might be useful for some scenarios)
Service : [Service ID, Priority, ...] (Not clear how to use it but
seems useful)
Traffic: [T0, T1, T2, ..., TN]
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Message :
Request: ask for the data
Reply: Data
Notice: For notification or others
Policy: Control policy
2.2.2. QoS management
It is worthy to predict the traffic change for avoiding the
congestion and ensuring QoS. As the following figure shown, the AI
system continuously collects link status data from the network. This
AI system is responsible for two things. One is monitoring and
predicting the traffic on each link and the other one is calculating
the usable route for any pair of nodes according to the prediction
and current link status. Assume that there is a VPN named VPN_S_D
from node S to D which pass through S-A-B-C-D. According to the
prediction, there will be a huge traffic flow from node A to C in the
future 10 min. The traffic will increase the end-to-end delay from S
to D so that the QoS will not be ensured.
x x
_ A ---- B ---- C._ link status +----------+
,' \ / `. =============>|IDN Engine|
-' \ / `- +----------+
S ------I ---- J ---- K ---- D
. / \ ,'
`. / \ ,'
' O ---- P ---- Q '
There are at least two solutions. one is modifying the object's
configuration to avoid the potential congestion. For example, we
modify the VPN_S_D route from S-A-B-C-D to S-I-J-K-D. The other one
is restricting non-object's transmission so that to protect the
object's QoS. For example, we increase the reserved bandwidth of
VPN_S_D or modify the route of non-object flows from S-A-B-C-D to
S-I-J-K-D therefore most of the traffic will not affect VPN_S_D.
Here we may have some challenges. Challenge 1 is the AI prediction
and autonomic decision should be a quick response. The whole process
must be finished before the congestion happens meanwhile the AI
system is meaningless. The question is how to implement such quick
response? Challenge 2 is whether there is existing protocols which
can support high frequency measurement? Because AI system needs to
be fed with continuous link status data. And the real-time data need
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to be captured frequently otherwise the route change will be
worthless. I think the protocols that support high frequency
measurement and data collection may become one of our focus point.
The process, data format and message needs are:
Process: 1. Data capture (e.g. traffic sample of physical/logical
port ); 2. Training Model; 3. Real-time data capture and input; 4.
Output percentages; 5. Fix error and go back to 3.
Data Format:
Time : [Timestamp, Value type (Delay/Packet Loss/...), Unit,
Number of Value, Sampling Period]
Position: [Link ID, Device ID]
Value: [V0, V1, V2, ..., VN]
Message :
Request: ask for the data
Reply: Data
Notice: For notification or others
Policy: Control policy
2.2.3. Deep Reinforcement-Learning Control of the Network
Recently important breakthroughs have been achieved in the area Deep-
Reinforcement Learning (DRL) [REF1] architectures where agents can be
trained online to operate complex environments and achieve quasi-
optimal configurations. In this context, a DRL can be used to
control the routing of the network and achieve the target policy set
by the administrators (e.g., [REF2, REF3, REF4]).
The following figure describes a common architecture of a DRL
operating a network. The agent acts upon the network (action) by
changing the configuration, this results in the network changing its
fundamental state (e.g, different per-link utilization and a
different traffic load). Finally, the reward function is defined by
the operator and represents the target performance (e.g., load-
balance the traffic in the network). The agent will learn how to act
upon the network to maximize the expected reward function.
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+---------------+
+------------------> |
| | Agent +---------------------+
| +---------------> | |
| | +---------------+ |
| | |
State | | |
| | Reward Function (Policy) Action |
| | |
| | |
| | |
| | +------------------------------------+ |
| +----+ | |
+-------+ Network <-----------+
| |
+------------------------------------+
The main operational advantages of DRL agents with respect to
existing optimization techniques are:
1. DRL are able to learn and generalize from past experience to
provide solutions to unseen scenarios. This is not possible
using existing optimization techniques that do not learn from the
past.
2. Once trained, either offline or online, DRL agents can optimize
in one single step. On the contrary, existing optimization
techniques require to run iteratively each time a new scenario is
found, for instance when a link goes down or the traffic changes
in a significant way. It is worth noting that a common practice
is to run such techniques in advance of common scenarios and
store their resulting configurations, however it is very complex
to consider all the potential scenarios.
3. DRL agents see the network as a black-box and do no need any
prior assumption about the system. However heuristics, very
commonly used in optimization strategies, are tailored for the
problem they are trying to optimize. However, an operator only
needs to change the reward function to implement a different
target network policy.
In what follows we describe the process, data format and messages
needed assuming a DRL agent that seeks to load-balance the traffic of
the network that is, to minimize the maximum loaded link. This is a
very common optimization strategy.
Process: 1.- Act upon the network by changing the routing
configuration, for instance using a standard mechanism. 2.- Receive
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the state of the network, this is the per-link delay and the current
traffic load. 3.- Compute the reward function as a function of the
state. 4.- Deep Reinforcement Learning training. 5.- Go back to step
1.
Data Format
(state) Per-Link Utilization: [link id, utilization, averaging
time]
(action) Change on the routing configuration. This can be done
through the SDN controller and/or other standard mechanisms.
(reward) This is an algorithm that has as input the state and as
output a value that represents how close we are to the target
policy set by the operator. More about this can be found in the
next section.
Messages:
State: Measure the per-link utilization
Action: Change the routing configuration
2.2.3.1. The Reward Function as the Network Policy
The agent seek to maximize the expected reward function and it
represents the target policy that the agent will aim to achieve and
configure on the network. In this context the reward function is the
mathematical representation of the target network policy. However,
the entire architecture includes a set of different pieces that may
come from different vendors but must interoperate, the pieces are:
the agent itself, the reward function and the state. This requires
the following standardization efforts:
1. The reward function and its translation from the human-readable
target network policy. The operators may want to use different
vendor DRL agents that need to understand the reward function.
Please note that the reward function depends on the
representation of the state.
2. The state includes monitoring information about the network, such
as the per-link utilization or the traffic load. Since the state
is an input of the agent and is used in the reward function,
there is a need for standard representation so that the different
pieces can interoperate.
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2.2.4. QoE Management via Supervised Learning
Networks can measure low-level metrics, such as delay, jitter and
losses. However users perceive the performance of the network based
on QoE metrics, such as Mean Opinion Scores. Unfortunately, QoE
metrics cannot be typically directly measured over the wire and as
such, need the subjective views of the users. The challenge is then
to operate the network based on low-level metrics while fulfilling
non-measurable QoE metrics. One of the main reason behind this
challenge is that the relationship between the low-level and the QoE
metrics are very complex, i.e. multi-dimensional and non-lineal.
+-------------+ +---------------------+
| Supervised | Extract |Relation between QoE |
| Learning +-Knowledge-->and low-level network+-------+
| | |metrics | |
+------^------+ +---------------------+ |
+ |
Learn |
| Install Knowledge
| |
+----------+--------------+ +-----------------v-----+
| Network Analytics | | |
| (including Ground Truth)| | Network Management |
| | | |
+----------+--------------+ +-----------------------+
^ |
| |
| +-------------+ |
| | | |
+-----Monitor-------+ Network <----Operate----+
| |
+-------------+
For this a well-established technique (e.g., see [REF5] and the
references therein) is to follow the architecture depicted in the
following figure. First the network low-level metrics are measured
using telemetry, this information is stored in the Network Analytics
platform. In addition to this users and or applications are polled
to obtain QoE metrics of the network. The data-set containing both
the low-level metrics and the QoE metrics is considered the ground
truth.
By means of supervised learning (e.g., deep neural networks) we aim
to learn the relation between the low-level and the QoE metrics. As
an example we aim to learn the relation between the amounts of losses
in different wireless links, the SNR and the utilization with the
perceived MoS. Typically it has been shown that such relationship is
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non-lineal and multi-dimensional and as such, can be understood by a
neural network. This relationship is the knowledge that we extract
from the ground truth and it is used by the Network Management (NM)
module. By means of this knowledge, the NM can understand how to
operate the network based on low-level metrics (e.g., keep losses
below a certain threshold) to fulfill QoE requirements.
2.2.5. TBD
3. Measurement and Data Format
TBD
3.1. Measurement Tools and Methods
The modern AI algorithms are mostly based on data-driven, which means
that the AI engine needs quite plenty of data to feed and upgrade.
In other words, higher frequency and accuracy data is required. The
high scalability requirement needs distributed measurement tools to
provide such abilities. The traditional methods and improvements may
hardly support.
Firstly, the current measurement methods mostly orient to the
service. For example, the voice service requires the end to end
delay and jitter in a low level. Besides that, the AI engine may
need more data from both network and other sources. For example, the
QoE and identity information may influence the AI engine to make
different decisions. The current measurement tools and data model
cannot support this ability. Thus, the potential usable tools and
methods, such as high frequency, high precision, new KPIs and so on,
may need to develop.
Secondly, the current measurement methods mostly cannot support high
frequency measurement. Even though it can, the data feedback scheme
is commonly closed. The word "closed" means that the measured data
is commonly sent to the device which launches the measure action
rather than the data demander (AI Engine). The future measurement
tools require more programmability, especially in the data feedback
scheme.
TBD.
3.2. Data Format Analysis
There is huge gap between the current network data and algorithm
data. The network data, such as IP address, delay, link utilization
and etc., is mostly semantic. It means that each data actually
describe a specific physical or logical entity. For example, one IP
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address means a certain location or a certain host in the network.
However, the input and output data of an algorithm is usually non-
semantic, which means it is not responding to a specific
concept/action/device that can be found in the network. This depends
on the fundamental design of AI algorithm and is hardly changed in
the short term.
Another issue is that the AI engine potentially needs to obtain data
from external sources. For the data that can be provided one-off, it
is easily solved according to the application. For the data that
needs to be provided continuously (e.g. the real-time external data),
it is required to define the data format that satisfy the algorithm.
Similarly, the output of algorithm may need to be translated into
specific format that the next step devices can run and execute.
Otherwise, it is hard to build up the full autonomic close loop of
the network management. In other words, the data aggregation process
is important and it is valuable to build the bridge between the
network data and algorithm data.
TBD.
4. Benchmarking Framework
A standard benchmarking framework is required to assess the quality
of an AI mechanism when it is used to resolve a specific problem in
the network management and control area. It comprises a reference
set of procedures, methods, models, and boundary values that *must*
be enforced to the benchmarked mechanism, so that its operation can
be comparable to other mechanisms and users can easily understand
what to expect from each one.
Moreover, both the metrics included as a reference within the
benchmarking framework and the results obtained from its application
to a new mechanism must follow a standard format. Therefore, the
standard formats must be enforced to all data, either being
introduced to the benchmarking application or system (consumed), or
obtained from its application (produced).
A common and decentralized "data market" can (and would) arise from
the inclusion, dependency, and the general relation of all data,
considering it is represented using the same concepts (ontology) and
the standard format mentioned here. As a reference, it is worth to
mention that a similar approach has been already applied to genome
and protein data to build standardized and easily transferable data
banks [PMJ1][PMJ2] [PMJ3], and they have demonstrated to be key
enablers in their respective work areas.
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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.
5. References Model and Potential Standardization Points
5.1. References Model
A three layers reference model of IDN has been proposed as follow.
This architecture can cover, explain and support most of the current
use cases and scenarios.
+-----------+ +----------+
|Open |------------------------->| |
|Application| +---------------------+3rd Party |-+
|Interface | | IDN Engine |Algorithm | |
+-----------+ | +---------+ +-----+ |Interface | |
+------------+ | |Algorithm| |Model| | | |
|Data Refiner+-->| +---------+ +-----+ +----------- |
+------------+ +----------------------------------+
^ | Training | Inference |
Intelligent | +----------------------------------+
Layer +-----------------+ |
| | v
+-------------+ +-------------+ +-------------+
|External Data| |Internal Data| | Policy |
|Interface | |Interface | | Generator |
+-------------+ +-------------+ +-------------+
^ ^ |
| | v
+----------+ +-------------+ +----------------+
Control |3rd Party | |Aggregating |--->|Control Function|
Layer |Dataset | |Dataset | +----------------+
+----------+ +-------------+ | Inference |
^ ^ +----------------+
| | |
| | |
| | v
+-------------+ +-----------+ +------------+
Infras- |Terminal/User| |Measurement| | Network |
tructure|Device |--->|Function |<-----| Function |
Layer +-------------+ +-----------+ +------------+
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The under layer is Infrastructure layer, which contains network
function, measurement function and terminal/user device. The network
function stands for the traditional routers, switches and other
network devices, which are responsible for constructing the network
foundations and forwarding data. The Measurement function stands for
devices that can collect information from the network and various
devices. A popular option are probe system, which is deployed
distributed among the network. Besides that, some of the network
devices integrate the measure function and play two roles. The
information may involve but not limited the content listed in
following table. The Terminal/User Device stands for the device that
produces and consumes data, which may include PC, smart phone,
datacenter, content storage server, cloud and etc. Some of the data
produced by terminal/user devices is measurable. This type of data
will be captured by the measurement function. Other types of data
that cannot be measured directly by network measurement functions is
represented as 3rd party datasets, which hopefully can be utilized in
the future via 3rd party integration at the intelligence layer.
-----------------------------------------------------------------
Type Content
-----------------------------------------------------------------
Network Data Delay, Jitter, Packet Lose Rate,
Link Utilization, ...
Device Data Device Configuration, VPN Configuration,
Slicing Configuration, ...
User Data QoE Feedback, User Information, ...
Data Packet Packet Sample, Packet Character, ...
Other Type TBD
-----------------------------------------------------------------
The middle layer is Control Layer, which contains Control Function,
Dataset Aggregation (Function) and 3rd Party Dataset. The control
function stands for entities that can control, configure and operate
devices, especially network devices. In SDN, controller and
orchestrator are control functions. Classical network devices such
as routers integrate the forwarding and control functions (although
as of today not with many instances of intelligent control
functions). Classical routers therefore include functions from two
layers. We foresee that the control function will most likely only
perform intelligent inference, but not learn. For example, to
execute neural networks, but do not train them. This is only an
assumption at this time though and may prove to be wrong in the
future when training becomes something easier defined into the
control layer.
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The aggregated dataset function owns the ability to gather and tidy
the data. The database or database cluster is the typical example.
Some of the control devices, such as SDN controller, integrate this
function. Distributed instances aggregate data have also been
defined. The network data can be directly sent back to the control
function in support of network policies. For example, the controller
can adjust the flow table according to the local cache which collects
the network data periodically from the devices in its controlled
area. The 3rd party dataset involves the data that may be provided
by all kinds of applications or services. For example, the content
provider may own social contact data and the map service provider may
own the geographic data. This information does not belong to the
network but could be very helpful for intelligent analytics and
decision making in the network - which is why we device in the
architecture the ability to communicate it between 3rd parties and
the network.
The high layer, which is also the main body of IDN, is the
Intelligence Layer. This layer is commonly deployed in the
datacenter, or large scale computing centre that can support massive
storage and computing resources. To the south direction, there are
two interfaces which provides external data (3rd party data oriented)
and internal data (network data oriented) access. We define a data
refiner component to emphasize the need to adopt format and structure
of various types of collected information to the needs of the IDN
Engine.
The core of the IDN Engine are algorithm and model. The IDN Engine
can be built based on the result of the large body of research and
platform development work that already exists (albeit mostly
developed for and deployed with non-network data). The platform
should be agile extensible for future services, therefore we define a
3rd party Algorithm Interface to provide an adaptive developing
ability. The user (or a 3rd party) may develop his/her own
algorithms and upload then onto the IDN Engine via a northbound Open
Application Interface. Additional Northbound Open Application
interfaces can also be used to connect other software platforms to
the IDN Engine to create a cooperation between multiple systems (not
shown).
The output of IDN Engine is transmitted to the Policy Generator.
Since the policy language might be machine readable or unreadable,
the Policy Generator is responsible for generating the executable
commands and connect to the control devices. This process refers to
the interactions of northbound interface of control devices - which
is what often gets standardized. Therefore, some of the potential
standardization points will be mentioned in the following.
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5.2. Measurement
In IDN, the intelligent system (or database) needs frequent and
repeat measurement to obtain the link information. A fast measure
and feedback protocol is needed to meet the requirement of
measurement and data collecting. It may be based on SNMP or an
absolutely new protocol. The intelligent system needs massive data
to feed and support to formulate the policy and decision. Therefore,
the measurement must be satisfy the data requirement of IDN.
Firstly, there may be higher-level requirement for the existing
measuring technology. The high timeliness is one of the potential
point. The IDN's control function needs accurate, global and highly
real-time network data support. The current measure technology can
only satisfy at least two characters of the three. Secondly, the IDN
may need more kinds of data type to measure. Not only the delay,
jitter and packet loss rate, but also the link utilization and other
necessary parameters.
5.3. Data representation, transport and aggregation
The data representation is significant. Most of the current AI
algorithms were born in the pattern recognition area, especially the
image processing. The advantage of these algorithms is that they are
very good at dealing with complex problems, especially mining and
modeling the hidden relationship among the non-semantic data. One of
the disadvantages is that almost all the algorithms require the
training data has a high concordance. Fortunately, the image file
instinctively owns this character. All the images can be expressed
as uniform binary vectors or can be easily transformed into uniform
format. But this condition is hardly satisfied in network area.
A uniform data format is required, which can implement the
justification, correlation and affiliation of the data. Which may
obtain the best performance of AI algorithm to mine the valid pattern
hidden in the data. Since the intelligent system is data-driven, and
the data resources are from different kind of vendors and device
types, the data representation SHALL be consistent so that the
intelligent system could merge the data and do the analysis/learning.
Also, the data collection interface might also need to be
standardized so that the interface is able to get the data the
intelligent system needs.
Moreover, it is significant to standard the policy representation.
Since there may multiply SDN controller system, a readable and
uniform policy representation is valuable to improve the policy
deploying efficiency and simplify the communication between
controllers on the East-West direction.
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5.4. Legacy Device Route control
Similar with IPv4/IPv6 transition, the IDN potentially faces to the
legacy problem, which means that the new devices and functions will
co-work with the legacy devices. Therefore, it is potentially
required to design the control protocols to solve the transition
problems.
5.5. TBD
TBD
6. Security Considerations
When security relevant decisions are made based on the use of
intelligent analytics or automated intelligent decision making, care
must be taken to understand the new security challenges. When for
example more intelligent decisions are enabled through the collection
of ever more data, it needs to be analyzed how that potentially
enables attackers to easier feed data that derails the intelligent
system ability to distinguish good from bad behavior.
TBD
7. IANA Considerations
There is no IANA action required by this document.
8. Acknowledgements
TBD
9. References
9.1. Normative References
[ISO_IEC10589]
"Intermediate system to Intermediate system intra-domain
routeing information exchange protocol for use in
conjunction with the protocol for providing the
connectionless-mode Network Service (ISO 8473), ISO/IEC
10589:2002, Second Edition.", Nov 2002.
[RFC1195] Callon, R., "Use of OSI IS-IS for routing in TCP/IP and
dual environments", RFC 1195, DOI 10.17487/RFC1195,
December 1990, <https://www.rfc-editor.org/info/rfc1195>.
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[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", BCP 14, RFC 2119,
DOI 10.17487/RFC2119, March 1997,
<https://www.rfc-editor.org/info/rfc2119>.
[RFC5301] McPherson, D. and N. Shen, "Dynamic Hostname Exchange
Mechanism for IS-IS", RFC 5301, DOI 10.17487/RFC5301,
October 2008, <https://www.rfc-editor.org/info/rfc5301>.
[RFC5304] Li, T. and R. Atkinson, "IS-IS Cryptographic
Authentication", RFC 5304, DOI 10.17487/RFC5304, October
2008, <https://www.rfc-editor.org/info/rfc5304>.
[RFC5305] Li, T. and H. Smit, "IS-IS Extensions for Traffic
Engineering", RFC 5305, DOI 10.17487/RFC5305, October
2008, <https://www.rfc-editor.org/info/rfc5305>.
[RFC5308] Hopps, C., "Routing IPv6 with IS-IS", RFC 5308,
DOI 10.17487/RFC5308, October 2008,
<https://www.rfc-editor.org/info/rfc5308>.
9.2. Informative References
[PMJ1] , <https://www.ncbi.nlm.nih.gov/genome/>.
[PMJ2] , <https://www.ncbi.nlm.nih.gov/genbank/>.
[PMJ3] , <https://www.rcsb.org/pdb/home/home.do>.
[REF1] "Human-level control through deep reinforcement learning.
Nature, 518(7540), pp.529-533.", 2015.
[REF2] "A Deep-Reinforcement Learning Approach for Software-
Defined Networking Routing Optimization. arXiv preprint
arXiv:1709.07080.", September 2017.
[REF3] "A roadmap for traffic engineering in SDN-OpenFlow
networks. Computer Networks, 71(C):1–30", October
2014.
[REF4] "Packet routing in dynamically changing networks: A
reinforcement learning approach. In Advances in neural
information processing systems, pages 671–678,",
1994.
[REF5] "A machine learning approach to classifying YouTube QoE
based on encrypted network traffic. Multimedia Tools and
Applications", January 2017.
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Authors' Addresses
Shen Yan
Huawei
Beiqing
Beijing, Haidian 100095
China
Email: yanshen@huawei.com
Pedro Martinez-Julia
NICT/Japan
Email: pedro@nict.go.jp
Albert Cabellos-Aparicio
Technical University of Catalonia
Email: albert.cabellos@gmail.com
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