Network Management Research Group                               M-S. Kim
Internet-Draft                                                      ETRI
Intended status: Informational                                  Y-H. Han
Expires: September 12, 2019                                    KoreaTech
                                                               Y-G. Hong
                                                          March 11, 2019

      Intelligent Reinforcement-learning-based Network Management


   This document presents intelligent network management scenarios based
   on reinforcement-learning approaches.  Nowadays, a heterogeneous
   network should usually provide real-time connectivity, the type of
   network management with the quality of real-time data, and
   transmission services generated by the operating system for 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.

Status of This Memo

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   This Internet-Draft will expire on September 12, 2019.

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Copyright Notice

   Copyright (c) 2019 IETF Trust and the persons identified as the
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   This document is subject to BCP 78 and the IETF Trust's Legal
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   ( in effect on the date of
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   described in the Simplified BSD License.

Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   3
   2.  Conventions and Terminology . . . . . . . . . . . . . . . . .   3
   3.  Theoretical Approaches  . . . . . . . . . . . . . . . . . . .   4
     3.1.  Reinforcement-learning  . . . . . . . . . . . . . . . . .   4
     3.2.  Deep-reinforcement-learning . . . . . . . . . . . . . . .   4
     3.3.  Advantage Actor Critic (A2C)  . . . . . . . . . . . . . .   4
     3.4.  Asynchronously Advantage Actor Critic (A3C) . . . . . . .   5
   4.  Reinforcement-learning-based process scenario . . . . . . . .   5
     4.1.  Single-agent with Single-model  . . . . . . . . . . . . .   6
     4.2.  Multi-agents Sharing Single-model . . . . . . . . . . . .   6
     4.3.  Adversarial Self-Play with Single-model . . . . . . . . .   6
     4.4.  Cooperative Multi-agents with Multiple-models . . . . . .   6
     4.5.  Competitive Multi-agents with Multiple-models . . . . . .   7
   5.  Use Cases . . . . . . . . . . . . . . . . . . . . . . . . . .   7
     5.1.  Intelligent Edge-computing for Traffic Control using
           Deep-reinforcement-learning . . . . . . . . . . . . . . .   7
     5.2.  Edge computing system in a field of Construction-site
           using Reinforcement-learning  . . . . . . . . . . . . . .   7
     5.3.  Deep-reinforcement-learning-based Cyber Physical
           Management Control system over a network  . . . . . . . .   8
   6.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .   9
   7.  Security Considerations . . . . . . . . . . . . . . . . . . .   9
   8.  References  . . . . . . . . . . . . . . . . . . . . . . . . .   9
     8.1.  Normative References  . . . . . . . . . . . . . . . . . .   9
     8.2.  Informative References  . . . . . . . . . . . . . . . . .   9
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  11

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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]

2.  Conventions and Terminology

   The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
   document are to be interpreted as described in [RFC2119].

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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.

   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

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   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
   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].

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
   to the agents.  The followings are possible RL training scenarios for
   network management.

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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

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

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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-

   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

   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 Cyber Physical Management
      Control system over a 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

6.  IANA Considerations

   There are no IANA considerations related to this document.

7.  Security Considerations


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,

8.2.  Informative References

              Jiang, S., "Network Machine Learning", ID draft-jiang-
              nmlrg-network-machine-learning-02, October 2016.

              "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.

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              "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.

   [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.

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              "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, 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.

   [Stampa]   "G Stamp, M Arias, etc., A Deep-reinforcement Learning
              Approach for Software-defined Networking Routing
              Optimization, cs.NI", 2017.

              "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 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

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   Min-Suk Kim
   161 Gajeong-Dong Yuseung-Gu
   Daejeon  305-700

   Phone: +82 42 860 5930

   Youn-Hee Han
   Byeongcheon-myeon Gajeon-ri, Dongnam-gu
   Choenan-si, Chungcheongnam-do

   Phone: +82 41 560 1486

   Yong-Geun Hong
   161 Gajeong-Dong Yuseung-Gu
   Daejeon  305-700

   Phone: +82 42 860 6557

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