Internet DRAFT - draft-kim-nmrg-rl
draft-kim-nmrg-rl
Network Management Research Group M-S. Kim
Internet-Draft ETRI
Intended status: Informational Y-H. Han
Expires: January 9, 2020 KoreaTech
Y-G. Hong
ETRI
July 8, 2019
Intelligent Reinforcement-learning-based Network Management
draft-kim-nmrg-rl-05
Abstract
This document presents intelligent network management based on
Artificial Intelligent (AI) such as reinforcement-learning
approaches. In a heterogeneous network, intelligent management with
Artificial Intelligent should usually provide real-time connectivity,
the type of network management with the quality of real-time data,
and transmission services generated by an application service. With
that reason intelligent management system is needed to support real-
time connection and protection through efficient management of
interfering network traffic for high-quality network data
transmission in the both cloud and IoE network systems.
Reinforcement-learning is one of the machine learning algorithms that
can intelligently and autonomously provide to management systems over
a communication network. Reinforcement-learning has developed and
expanded with deep learning technique based on model-driven or data-
driven technical approaches so that these trendy techniques have been
widely to intelligently attempt an adaptive networking models with
effective strategies in environmental disturbances over variety of
networking areas. For Network AI with the intelligent and effective
strategies, intent-based network (IBN) can be also considered to
continuously and automatically evaluate network status under required
policy for dynamic network optimization. The key element for the
intent-based network is that it provides a verification of whether
the represented network intent is implementable or currently
implemented in the network. Additionally, this approach need to
provide to take action in real time if the desired network state and
actual state are inconsistent.
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
Task Force (IETF). Note that other groups may also distribute
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This Internet-Draft will expire on January 9, 2020.
Copyright Notice
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 3
2. Conventions and Terminology . . . . . . . . . . . . . . . . . 4
3. Theoretical Approaches . . . . . . . . . . . . . . . . . . . 4
3.1. Reinforcement-learning . . . . . . . . . . . . . . . . . 4
3.2. Deep-reinforcement-learning . . . . . . . . . . . . . . . 4
3.3. Advantage Actor Critic (A2C) . . . . . . . . . . . . . . 5
3.4. Asynchronously Advantage Actor Critic (A3C) . . . . . . . 5
3.5. Intent-based Network (IBN) . . . . . . . . . . . . . . . 6
4. Reinforcement-learning-based process scenario . . . . . . . . 6
4.1. Single-agent with Single-model . . . . . . . . . . . . . 7
4.2. Multi-agents Sharing Single-model . . . . . . . . . . . . 7
4.3. Adversarial Self-Play with Single-model . . . . . . . . . 7
4.4. Cooperative Multi-agents with Multiple-models . . . . . . 7
4.5. Competitive Multi-agents with Multiple-models . . . . . . 8
5. Use Cases . . . . . . . . . . . . . . . . . . . . . . . . . . 8
5.1. Intelligent Edge-computing for Traffic Control using
Deep-reinforcement-learning . . . . . . . . . . . . . . . 8
5.2. Edge computing system in a field of Construction-site
using Reinforcement-learning . . . . . . . . . . . . . . 8
5.3. Deep-reinforcement-learning-based remote Control system
over a software-defined network . . . . . . . . . . . . . 9
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6. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 11
7. Security Considerations . . . . . . . . . . . . . . . . . . . 11
8. References . . . . . . . . . . . . . . . . . . . . . . . . . 11
8.1. Normative References . . . . . . . . . . . . . . . . . . 11
8.2. Informative References . . . . . . . . . . . . . . . . . 11
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 13
1. Introduction
Reinforcement-learning for intelligently autonomous network
management, in general, is one of the challengeable methods in a
dynamic complex and cluttered network environments. With the
intelligent approach needs the development of computational systems
in a single or large distributed networking nodes, where these
environments involve limited and incomplete knowledge.
The reinforcement-learning can become a challenge-able and effective
technique to transfer and share information via the global
environment, as it does not require a priori-knowledge of the agent
behavior or environment to accomplish its tasks [Megherbi]. Such a
knowledge is usually acquired and learned repeatedly and autonomously
by trial and error. The reinforcement-learning is also one of the
machine learning techniques that will be adapted to the various
networking environments for automatic networks [S.Jiang].
Deep-reinforcement-learning recently proposes has been extended from
reinforcement-learning that can emerge as more powerful model-driven
or data-driven model in a large state space, to overcome the
classical behavior reinforcement-learning process. However, the
classical reinforcement-learning slightly has a limitation to be
adopted in networking areas, since the networking environments
consist of significantly large and complex components in fields of
routing configuration, optimization and system management, so that
deep-reinforcement-learning can provide much more state information
for learning process.[MS]
There are many different networking management problems to
intelligently solve, such as connectivity, traffic management, fast
Internet without latency and etc. Reinforcement-learning-based
approaches can surely provide some of specific solutions with
multiple cases against human operating capacities although it is a
challengeable area due to a multitude of reasons such as large state
space, complexity in the giving reward, difficulty in control
actions, and difficulty in sharing and merging of the trained
knowledge in a distributed memory node to be transferred over a
communication network.[MS]
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In addition, Intent-based network bridge to solve some of network
problems and gaps between network business model and technical
scheme. Intents should be applied to application service levels,
security policies, compliance, operational processes, and other
business needs. The network should constantly monitor and adjust to
meet the intent in following the monitoring system. There are some
of requirements to satisfy Intent-based network as following: (1)
Transfer, (2) policy activation (automatically), (3) guarantee
(Continuous monitoring and verification) [Cisco]. Through
continuously monitoring with network data, we are able to collect
network information and to analyze the collected information by
artificial intelligent approach. If the analysis result shows that
the new network configuration parameter needs to be changed or
reconfigured by deriving the optimized value.
2. Conventions and Terminology
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
"SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
document are to be interpreted as described in [RFC2119].
3. Theoretical Approaches
3.1. Reinforcement-learning
Reinforcement-learning is an area of machine learning concerned with
how software agents should take actions in an environment so as to
maximize some notion of cumulative reward.[Wikipedia] The
reinforcement-learning is normally used with a reward from
centralized node (the global brain), and capable of autonomous
acquirement and incorporation of knowledge. It is continuously self-
improving and becoming more efficient as the learning process from an
agent experience to optimize management performance for autonomous
learning process.[Sutton][Madera]
3.2. Deep-reinforcement-learning
Some of advanced techniques using reinforcement-learning encounter
and combine with deep-learning in neural networks that has made it
possible to extract high-level features from raw data in compute
vision [A Krizhevsky]. There are many challenges under the deep-
learning models such as convolution neural network, recurrent neural
network and etc., on the reinforcement-learning approach. The
benefit of the deep learning applications is that lots of networking
models, but the problematic issue is complex and cluttered networking
structures used with large amounts of labelled training data.
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Recently, the advances in training deep neural networks to develop a
novel artificial agent, termed a deep Q-network (deep-reinforcement-
learning network), can be used to learn successful policies directly
from high-dimensional sensory inputs using end-to-end reinforcement
learning [V.Mnih].
The deep-reinforcement-learning (deep Q-network) can provide more
extended and powerful scenarios to build networking models with
optimized action controls, huge system states and real-time-based
reward function. Moreover, the technique has a significant advantage
to set highly sequential data in a large model state space. [MS] In
particular, the data distribution in reinforcement-learning is able
to change as learning behaviors, that is a problem for deep learning
approaches assumed by a fixed underlying distribution [V. Mnih].
3.3. Advantage Actor Critic (A2C)
Advantage Actor Critic is one of the intelligent reinforcement-
learning models based on policy gradient model. The intelligent
approach can optimize deep neural network controller in terms of
reinforcement-learning algorithms, and show that parallel actor-
learners have a stabilizing effect on training and they can be
allowing all of the methods to successfully train neural network
controllers [Volodymyr Mnih]. Even if the prior deep-reinforcement-
learning algorithm with experience replay memory tremendously has
performance in challenging of the control service domains, it still
needs to use more memory and computational power due to off-policy
learning methods. To make up for this algorithms, a new algorithm
has appeared.
The Advantage Actor Critic (consisting of actor and critic) method
would implement generalized policy iteration alternating between a
policy evaluation and a policy improvement step. Actor is a policy-
based method that can improve the current policy for available the
best next action. Critic in the value-based approach can evaluate
the current policy and reduce the variance by a bootstrapping method.
It is more stable and effective algorithm than the pure policy-based
gradient methods.[MS]
3.4. Asynchronously Advantage Actor Critic (A3C)
Asynchronously Advantage Actor Critic is the updated algorithm based
on Advantage Actor Critic. The main algorithm concept is to run
multiple environments in parallel to run the agent asynchronously
instead of experience replay. The parallel environment reduces the
correlation of agent's data and induces each agent to experience
various states so that the learning process can become a stationary
process. This algorithm is a beneficial and practical point of view
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since it allows learning performance even with a general multi-core
CPU. In addition, it can be applied to continuous space as well as
discrete action space, and also has the advantages of learning both
feedforward and recurrent agent.[MS]
A3C algorithm is possibly a number of complementary improvement to
the neural network architecture and it has been shown to accurately
produce and estimate of Q-values by including separate streams for
the state value and advantage in the network to improve both value-
based and policy-based methods by making it easier for the network to
represent feature coordinates [Volodymyr Mnih].
3.5. Intent-based Network (IBN)
ntent-based Network is a new technical approach that can adapt the
network flexibly through configuration parameters derived from data
analysis for network machine learning. Software-defined Networking
(SDN) is a similar concept with Intent-based Network, however,
Software-defined Networking has not yet tipped in the sector that
relies on network automation. With the approach, network machine
learning is integrated with network analysis, routing, wireless
communications, and resource management. However, unlike the field
of computer vision, which can easily acquire sufficient data, it is
difficult to obtain data over a real network. Therefore, there are
limitations to apply machine learning technique to network field with
the data. Reinforcement Learning (RL) can diminish much attention
and the importance of securing high-quality data, so that both
concepts of reinforcement learning and intent-based network might
solve the limitation and integrate a gap between network machine
learning and network technique.
Intent-based network is also describing how to apply the setting
values for network management/operation in a procedural way. For
that reason, the approach is also the core of Intent processing that
automatically interprets it and declares it declaratively. Even if
the basic concepts of intent-based network reflects and to be
announced regarding intent, there is no standardized form of Intent
processing technology. While intent-based network has the advantage
of providing a higher level of abstraction in network management/
operation and providing ease of use, a more specific and clear
definition of the technology is likely to be needed.
4. Reinforcement-learning-based process scenario
With a single agent or multiple agents trained for intelligent
network management, a variety of training scenarios are possible,
depending on how agents are interacted and how many models are linked
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to the agents. The followings are possible RL training scenarios for
network management.
4.1. Single-agent with Single-model
This is the traditional scenario of training a single agent who tries
to achieve one goal related to network management. It receives all
of information and rewards from a network (or a simulated network),
and decides its appropriate action for the current network status.
4.2. Multi-agents Sharing Single-model
In this scenario, multiple agents share a single model and a single
goal linked to the model. But, each of them is connected to an
independent part of network or an independent whole network, so that
they receive different information and rewards from such an
independent one. The multiple agents experience differently on their
connected networks. However, it does not mean their training
behavior for network management will diverge. Each of their
experience is used to train the single model. This scenario is a
kind of parallelized version of the traditional 'Single-Agent with
Single-Model' scenario, which can speed-up the RL training process
and stabilize the single model's behavior.
4.3. Adversarial Self-Play with Single-model
This scenario contains two interacting agents with inverse reward
functions linked to a single model. This scenario makes an agent
have the perfectly matched opposing agent: itself, and trains the
agent to become increasingly more skilled for network management.
Inverse rewards are used to punish the opposing agent when an agent
receives as positive reward, and vice versa. The two agents are
linked to a single model for network management, and the model are
trained and stabilized while both agents interact in a conflicting
manner.
4.4. Cooperative Multi-agents with Multiple-models
In this scenario, two or more interacting agents share a common
reward function linked to multiple different models for network
management. In this scenario, a common goal is set up and all agents
are trained to achieve the goal together that is hard to be achieved
alone. Usually, each agent has access only to partial information of
network status and determines an appropriate action by using its own
model. Each of actions will be independently taken in order to
accomplish a management task and collaboratively achieve the common
goal.
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4.5. Competitive Multi-agents with Multiple-models
This scenario contains two or more interacting agents with diverse
reward function linked to multiple different models. In this
scenario, agents will compete with one another to obtain some limited
set of network resources and try to achieve their own goal. In a
network, there will be tasks that have different management
objectives. This leads multi-objective optimization problems, which
are generally difficult to solve analytically. This scenario is
suitable for solving such a multi-objective optimization problem
related to network management by allowing each agent solve a single-
objective problem, but complete with each other.
5. Use Cases
5.1. Intelligent Edge-computing for Traffic Control using Deep-
reinforcement-learning
Edge computing is a concept that allows data from a variety of
devices to be directly analyzed at the site or near the data, rather
than being sent to a centralized data center such as the cloud. As
such, edge computing will support data flow acceleration by
processing data with low latency in real-time. In addition, by
supporting efficient data processing on large amounts of data that
can be processed around the source, and internet bandwidth usage will
be also reduced.
Deep-reinforcement-learning would be useful technique to improve
system performance in an intelligent edge-controlled service system
for fast response time, reliability and security. Deep-
reinforcement-learning is model-free approach so that many algorithms
such as DQN, A2C and A3C can be adopted to resolve network problems
in time-sensitive systems.
5.2. Edge computing system in a field of Construction-site using
Reinforcement-learning
In a construction site, there are many dangerous elements such as
noisy, gas leak and vibration needed by alerts, so that real-time
monitoring system to detect the alerts using machine learning
techniques can provide more effective solution and approach to
recognize dangerous construction elements.
Representatively, to monitor these elements CCTV (closed-circuit
television) should be locally and continuously broadcasting in a
situation of construction site. At that time, it is in-effective and
wasteful even if the CCTV is constantly broadcasting unchangeable
scenes in high definition. However, the streaming should be
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converted to high quality streaming data to rapidly show and defect
the dangerous situation, when any alert should be detected due to the
dangerous elements. To approach technically deep-reinforcement-
learning can provide a solution to automatically detect these kinds
of dangerous situations with prediction in an advance. It can also
provide the transform data including with the high-rate streaming
video and quickly prevent the other risks. Deep-reinforcement-
learning is an important role to efficiently manage and monitor with
the given dataset in real-time.
5.3. Deep-reinforcement-learning-based remote Control system over a
software-defined network
With the nonlinear control system such as cyber physical system
provides an unstable system environment with initial control state
due to its nonlinear nature. In order to stably control the unstable
initial state, the prior-complex mathematical control methods (Linear
Quadratic Regulator, Proportional Integral Differential) are used for
successful control and management, but these approaches are needed
with difficult mathematical process and high-rate effort. Therefore,
using deep-reinforcement-learning can surely provide more effective
technical approach without difficult initial set of control states to
be compared with the other methods.
The ultimate purpose of the reinforcement-learning is to interact
with the environment and maximize the target reward value. Observing
the state in the step and the action by the policy are performed, and
the reward judge a value through the compensation given in the
environment. Deep-reinforcement-learning using Convolutional Neural
Network (CNN) can provide more performing learning process to make
stable control and management.
As part of the system, it shows how the physical environment and the
cyber environment interact with the reinforcement-learning module
over a network. The actions to control the physical environment,
delivered to the Enhanced Learning model based on DQN, transfer to
data to the physical environment using networking communication tools
as below.
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+-----Environment-----+ +---Control and Management---+
. . . .
. +-----------------+ . Network +--------------+ .
. . Physical System . .----------->. Cyber Module . .
. . . .<-----------. . .
. +-----------------+ . +--------------+ .
. . . . +--------+ .
+---------------------+ . .----------.RL Agent. .
. +--------+ .
+............................+
Figure 1: DRL-based Cyber Physical Management Control System
With the use-case, the reinforcement learning agent interacts with
the physical remote device while exchanging network packets. The
Software-defined network controller can manage the network traffic
transmission, so that the system is naturally composed of a cyber
environment and physical environment, and two environments closely
and synchronously.[Ju-Bong]
For the intelligent traffic management in the system, software-
defined networking for automation (basic concept for IBN) should be
used to control and manage of connection between the cyber physical
system and edge computing module. The intelligent approach consists
of software that intelligently controls the network and technique
that allows software to set up and control the network. The concept
of can be centralized to control of network operation by software
programming, centralizes switch/router control function based on
existing hardware. It is possible to manage the network according to
the requirements without the detailed network configuration.
In addition, software-defined networking switch is able to enable the
network traffic control to be controlled and managed by software-
based controllers. This approach is really similar with intent-based
networking since both approaches can share the similar principle
using software to run the network, however, intent-based networking
offers an abstraction layer under the implemented policy and
instruction across all the physical hardware within the
infrastructure for automated networking. To achieve superior intent-
based networking over a real network, the physical control system
will be implemented to automatically manage and provide IoE edge
smart traffic control service for high quality real time connection.
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6. IANA Considerations
There are no IANA considerations related to this document.
7. Security Considerations
[TBD]
8. References
8.1. Normative References
[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>.
8.2. Informative References
[I-D.jiang-nmlrg-network-machine-learning]
Jiang, S., "Network Machine Learning", ID draft-jiang-
nmlrg-network-machine-learning-02, October 2016.
[Megherbi]
"Megherbi, D. B., Kim, Minsuk, Madera, Manual., A Study of
Collaborative Distributed Multi-Goal and Multi-agent based
Systems for Large Critical Key Infrastructures and
Resources (CKIR) Dynamic Monitoring and Surveillance, IEEE
International Conference on Technologies for Homeland
Security", 2013.
[Teiralbar]
"Megherbi, D. B., Teiralbar, A. Boulenouar, J., A Time-
varying Environment Machine Learning Technique for
Autonomous Agent Shortest Path Planning, Proceedings of
SPIE International Conference on Signal and Image
Processing, Orlando, Florida", 2001.
[Nasim] "Nasim ArianpooEmail, Victor C.M. Leung, How network
monitoring and reinforcement learning can improve tcp
fairness in wireless multi-hop networks, EURASIP Journal
on Wireless Communications and Networking", 2016.
[Minsuk] "Dalila B. Megherbi and Minsuk Kim, A Hybrid P2P and
Master-Slave Cooperative Distributed Multi-Agent
Reinforcement Learning System with Asynchronously
Triggered Exploratory Trials and Clutter-index-based
Selected Sub goals, IEEE CIG Conference", 2016.
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[April] "April Yu, Raphael Palefsky-Smith, Rishi Bedi, Deep
Reinforcement Learning for Simulated Autonomous Vehicle
Control, Stanford University", 2016.
[Markus] "Markus Kuderer, Shilpa Gulati, Wolfram Burgard, Learning
Driving Styles for Autonomous Vehicles from Demonstration,
Robotics and Automation (ICRA)", 2015.
[Ann] "Ann Nowe, Peter Vrancx, Yann De Hauwere, Game Theory and
Multi-agent Reinforcement Learning, In book: Reinforcement
Learning: State of the Art, Edition: Adaptation, Learning,
and Optimization Volume 12", 2012.
[Kok-Lim] "Kok-Lim Alvin Yau, Hock Guan Goh, David Chieng, Kae
Hsiang Kwong, Application of Reinforcement Learning to
wireless sensor networks: models and algorithms, Published
in Journal Computing archive Volume 97 Issue 11, Pages
1045-1075", November 2015.
[Sutton] "Sutton, R. S., Barto, A. G., Reinforcement Learning: an
Introduction, MIT Press", 1998.
[Madera] "Madera, M., Megherbi, D. B., An Interconnected Dynamical
System Composed of Dynamics-based Reinforcement Learning
Agents in a Distributed Environment: A Case Study,
Proceedings IEEE International Conference on Computational
Intelligence for Measurement Systems and Applications,
Italy", 2012.
[Al-Dayaa]
"Al-Dayaa, H. S., Megherbi, D. B., Towards A Multiple-
Lookahead-Levels Reinforcement-Learning Technique and Its
Implementation in Integrated Circuits, Journal of
Artificial Intelligence, Journal of Supercomputing. Vol.
62, issue 1, pp. 588-61", 2012.
[Chowdappa]
"Chowdappa, Aswini., Skjellum, Anthony., Doss, Nathan,
Thread-Safe Message Passing with P4 and MPI, Technical
Report TR-CS-941025, Computer Science Department and NSF
Engineering Research Center, Mississippi State
University", 1994.
[Mnih] "V.Mnih and et al., Human-level Control Through Deep
Reinforcement Learning, Nature 518.7540", 2015.
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[Stampa] "G Stamp, M Arias, etc., A Deep-reinforcement Learning
Approach for Software-defined Networking Routing
Optimization, cs.NI", 2017.
[Krizhevsky]
"A Krizhevsky, I Sutskever, and G Hinton, Imagenet
classification with deep con- volutional neural networks,
In Advances in Neural Information Processing Systems,
1106-1114", 2012.
[Volodymyr]
"Volodymyr Mnih and et al., Asynchronous Methods for Deep
Reinforcement Learning, ICML, arXiv:1602.01783", 2016.
[MS] "Intelligent Network Management using Reinforcement-
learning, draft-kim-nmrg-rl-03", 2018.
[Ju-Bong] "Deep Q-Network Based Rotary Inverted Pendulum System and
Its Monitoring on the EdgeX Platform, International
Conference on Artificial Intelligence in Information and
Communication (ICAIIC)", 2019.
Authors' Addresses
Min-Suk Kim
Etri
161 Gajeong-Dong Yuseung-Gu
Daejeon 305-700
Korea
Phone: +82 42 860 5930
Email: mskim16@etri.re.kr
Youn-Hee Han
KoreaTech
Byeongcheon-myeon Gajeon-ri, Dongnam-gu
Choenan-si, Chungcheongnam-do
330-708
Korea
Phone: +82 41 560 1486
Email: yhhan@koreatech.ac.kr
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Yong-Geun Hong
ETRI
161 Gajeong-Dong Yuseung-Gu
Daejeon 305-700
Korea
Phone: +82 42 860 6557
Email: yghong@etri.re.kr
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