Internet Engineering Task Force I. Ullah
Internet-Draft Y-H. Han
Intended status: Informational KOREATECH
Expires: 23 October 2022 TY. Kim
ETRI
21 April 2022
Reinforcement Learning-Based Virtual Network Embedding: Problem
Statement
draft-ihsan-nmrg-rl-vne-ps-02
Abstract
In Network virtualization (NV) technology, Virtual Network Embedding
(VNE) is an algorithm used to map a virtual network to the substrate
network. VNE is the core orientation of NV which has a great impact
on the performance of virtual network and resource utilization of the
substrate network. An efficient embedding algorithm can maximize the
acceptance ratio of virtual networks to increase the revenue for
Internet service provider. Several works have been appeared on the
design of VNE solutions, however, it has becomes a challenging issues
for researchers. To solved the VNE problem, we believe that
reinforcement learning (RL) can play a vital role to make the VNE
algorithm more intelligent and efficient. Moreover, RL has been
merged with deep learning techniques to develop adaptive models with
effective strategies for various complex problems. In RL, agents can
learn desired behaviors (e.g, optimal VNE strategies), and after
learning and completing training, it can embed the virtual network to
the subtract network very quickly and efficiently. RL can reduce the
complexity of the VNE algorithm, however, it is too difficult to
apply RL techniques directly to VNE problems and need more research
study. In this document, we presenting a problem statement to
motivate the researchers toward the VNE problem using deep
reinforcement learning.
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Table of Contents
1. Introduction and Scope . . . . . . . . . . . . . . . . . . . 2
2. Reinforcement Learning-based VNE Solutions . . . . . . . . . 5
3. Terminology . . . . . . . . . . . . . . . . . . . . . . . . . 8
4. Problem Space . . . . . . . . . . . . . . . . . . . . . . . . 9
4.1. State Representation . . . . . . . . . . . . . . . . . . 9
4.2. Action Space . . . . . . . . . . . . . . . . . . . . . . 9
4.3. Reward Description . . . . . . . . . . . . . . . . . . . 10
4.4. Policy and RL Algorithms . . . . . . . . . . . . . . . . 11
4.5. Training Environment . . . . . . . . . . . . . . . . . . 12
4.6. Sim2Real Gap . . . . . . . . . . . . . . . . . . . . . . 13
4.7. Generalization . . . . . . . . . . . . . . . . . . . . . 14
5. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 14
6. Security Considerations . . . . . . . . . . . . . . . . . . . 14
7. Informative References . . . . . . . . . . . . . . . . . . . 14
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 18
1. Introduction and Scope
Recently, Network virtualization (NV) technology has received a lot
of attention from academics and industry. It allows multiple
heterogeneous virtual networks to share resources on the same
substrate network (SN) [RFC7364], [ASNVT2020]. The current large-
size fixed substrate network architecture is no longer efficient and
not extendable due to network ossification. To overcome this
limitations, traditional Internet Service Providers (ISPs) are
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divided into two independent parts which work together. One is the
Service Providers (SPs) who create and own the different number of
the VNs, and the other one is the Infrastructure Providers (InPs) who
own the SN devices and links as underlying resources. SPs generate
and construct the customized Virtual Network Requests (VNRs), and
lease the resources from InPs based on that requests. In addition,
two types of mediators can enter into the industry domain for better
coordination of SPs and InPs. One is the Virtual Network Providers
(VNPs) who assemble and coordinate diverse virtual resources from one
or more InPs, the other one is the Virtual Network Operators (VNOs)
who create, manage, and operate the VN according to the demand of the
SPs. VNPs and VNOs could enable efficient use of the physical
network and increase the commercial revenue of both SPs and InPs. NV
can increase network agility, flexibility and scalability while
creating significant cost savings. Greater network workload
mobility, increased availability of network resources with good
performance, and automated operations, are all the benefits of NV.
Virtual Network Embedding (VNE) [VNESURV2013] is one of the main
technique and strategy which used to map a virtual network to the
substrate network. VNE algorithm has two main parts, Node embedding:
where virtual nodes of VN have to be mapped to the SN nodes, and Link
embedding: where virtual links between the VNs have to be mapped to
the physical paths in the substrate network. It has been proven to
be NP-Hard, and both node and link embeddings have become challenging
for the researchers. A virtual node and link should be efficiently
embedded into a given SN, so that more VNR can be accepted with
minimum cost. The distance of the virtual nodes from each other in a
given SN is a big contribution to the link failures and causes the
rejection of VNRs. Hence, an efficient and intelligent technique is
required for VNE problem to reduce VNRs rejection [ENViNE2021]. In
the perspective of the InPs, the efficient VNE performs better mostly
in terms of revenue, acceptance ratio, and revenue-to-cost ratio.
Figure 1 shows the the example of two virtual network request VNR1
and VNR2 to embed them in the given substrate network. VNR1 contain
three virtual nodes (a, b, and c) with cpu demands (15, 30, and 10)
respectively, and the link between virtual the nodes a-b,b-c, and c-a
with bandwidth demands 15,20, and 35 respectively. Similarly, VNR2
contains virtual nodes and links with cpu and bandwidth demand
respectively. The purpose of the VNE algorithm to map the virtual
nodes and links of the VNRs to the physical nodes and links of the
given substrate as shown in Figure 1. [ENViNE2021].
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+----+ +----+ +----+ +----+
| a | | d | | e | | f |
| 15 | | 25 |__ _25___| 30 |__ _35_ __| 45 |
+----+ +----+ +----+ +----+
/ \ \ /
15 35 30 20
/ \ \ /
+----+ +----+ +----+ +----+
| b | | c | | g | | h |
| 30 |__ _20_ __ _| 10 | | 15 |__ _ __10__ __ __| 35 |
+----+ +----+ +----+ +----+
(VNR1) (VNR2)
|| Embedding || Embedding
VV VV
+----+ +----+ +----+ +----+
.......| a |......35......| c | | d |........25........| e |
: _____| 15 | | 10 |_______| 25 | ________| 30 |
: | +----+ +----+ +----+ | +----+
: | A | | : B | : | C | :
: | 50 |__ ___50__ __ __| : 60 |_:_ __30 _ _| 40 | :
: +__________+ +_:_________+ : +__________+ :
: | : | : | :
15 | : | : | 35
: 40 20 60 : 50 :
: | : | 30 | :
: | _:_____|_ : | :
+----:..............20........|.: | : | +----+
| b | | +----+.....30......|.........|....: | | f |
| 30 |_|___| g | | +----+ __|___| 45 |
+----+ | 15 |.....10......|.......| h |........20.....|......+----+
| D +____+ | E | 35 | | F |
| 50 |__ __ __ 70 _____| 40 +____+ ___ __ 50_ ___| 60 |
+__________+ +_________+ +__________+
Figure 1: Substrate network with embedded virtual network, VNR1
and VNR2
Recently, artificial intelligence and machine learning technologies
have been widely used to solve networking problems [SUR2018],
[MLCNM2018], [MVNNML2021]. There has been a surge in research
efforts,specially,reinforcement learning (RL) which has been
contributed much more in the many complex tasks, e.g. video games and
auto-driving etc. The main goal of an RL to learn better policies
for sequential decision making problems (e.g., VNE) and solve them
very efficiently.
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Problems such as node classification, pattern matching, and network
feature extraction, can be simplified by graph-related theories and
techniques. Graph neural network (GNN) is a new type of ML model
architecture that can aggregate graph features (degrees, distance to
specific nodes, node connectivity, etc.) on nodes [DVNEGCN2021].
Graph convolution neural network (GCNN) is a natural generalization
form of GNN which is used to automatically extract the features of
underlying network, which optimizes the selection of VNE decision.
The model can be used to cluster nodes and links according to the
physical nodes and physical links attribute characteristics (CPU,
storage, bandwidth, delay, etc.), and it is highly suitable for graph
structures of any topological form. Hence, GNN is useful to find the
best VNE strategy by intelligent agent training, and the organic
combination of VNE and GCN has a good prerequisite.
Designing and applying RL techniques directly into VNE problems is
not yet trivial, but may face several challenges. Several works have
been appeared on the design of VNE solutions using RL, which focuses
on how to interact with the environment to achieve maximum cumulative
return [VNEQS2021], [NRRL2020], [MVNE2020], [CDVNE2020], [PPRL2020],
[RLVNEWSN2020], [QLDC2019], [VNFFG2020], [VNEGCN2020], [NFVDeep2019],
[DeepViNE2019], [VNETD2019], [RDAM2018], [MOQL2018], [ZTORCH2018],
[NeuroViNE2018], [QVNE2020]. This document outlines the problems
encountered when designing and applying RL-based VNE solutions.
Section 2 describes how to design RL-based VNE solutions. Section 3
gives terminology, and Section 4 describes the problem space details.
2. Reinforcement Learning-based VNE Solutions
As we discussed that RL has been studied in various fields (such as
game, control system, operation research, information theory, multi-
agent system, network system, etc.) and shows better performance than
humans. Unlike deep learning, RL trains a policy model by receiving
rewards through interaction with the environment without training
label data.
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Recently, there have been several attempts to solve VNE problems
using RL. When applying RL-based algorithms to solve VNE problems,
the RL agent automatically learns through the environment without
human intervention. Once the agent completed the learning process,
it can generate the most appropriate embeddings decision (action)
based on the his knowledge and network state. For single embedding
or action at each time step the agent get reward from the
environments to adaptively train its policy for future action. The
RL agent gets the most optimized model based on the reward function
defined according to each objective (revenue, cost, revenue to cost
ratio and acceptance ratio). The optimal RL policy model provides
the VNE strategy appropriately according to the objective of the
network operator.
Figure 2. shows the virtual network embedding solution based on RL
algorithm. The RL strategy is divided into two main parts training
process and an inference process. In the training process, state
information is composed of various substrate networks and VNRs
(Environment), which are used as suitable inputs for RL models
through feature extraction. After that, the RL model is updated by
model updater using a feature extracted state and reward. In the
inference process, using the trained RL model, the embedding result
is provided to the operating network in real time.
The following figure shows the detail about RL method based virtual
networks embedding solutions.
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RL Model Training Process
+--------------------------------------------------------------------+
| Training Environment |
| +-------------------+ RL-based VNE Agent |
| | +---------+ | +----------------------------------+ |
| | | +---------+ | | Action | |
| | | | +----------+ |<----------------------------------+ | |
| | + | | Substrate| | | | | |
| | | | Networks | | | +----------+ +----------+ | |
| | + +----------+ | State | | Feature | | RL | | |
| | |----------->|Extraction|----->| Model | | |
| | +--------+ | | +----------+ | (Policy) | | |
| | | +---------+ | | | +----------+ | |
| | + | +---------+ | | | +---------+ A | |
| | + | VNRs | | Reward | +-->| Model | | | |
| | +---------+ |-------------------->| Updater |-----+ | |
| +-------------------+ | +---------+ | |
| +----------------------------------+ |
+--------------------------------------------------------------------+
|
Inference Process |
+---------------------------------V----------------------------------+
| + - - - - - - - + |
| Operating Network | RL Model | Trained RL Model |
| (Inference Environment) | Training |------------------+ |
| +-------------------+ | Process | | |
| | +-----------+ | + - - - - - - - + | |
| | | | | RL-based VNE Agent | |
| | | Substrate | | +----------------------------|-----+ |
| | | Network | | | Action | | |
| | | | |<--------------------------------+ | | |
| | +-----------+ | | | V | |
| | +---------+ | | +------------+ +---------+ | |
| | | +---------+ | State | | Feature | | Trained | | |
| | + | +----------+ |----------->| Extraction |---->| RL | | |
| | + | VNRs | | | +------------+ | Model | | |
| | +----------+ | | +---------+ | |
| +-------------------+ +----------------------------------+ |
+--------------------------------------------------------------------+
Figure 2: Two processes for RL method based VNE
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3. Terminology
Network Virtualization
Network virtualization is the process of combining hardware and
software network resources and network functionality into a
single, software-based administrative entity, a virtual network
[RFC7364].
Virtual Network Embedding (VNE)
Virtual Network Embedding (VNE) [VNESURV2013] is one of the main
techniques used to map a virtual network to the substrate network.
Substrate Network (SN)
The underlying physical network which contains the resources such
as CPU and bandwidth for virtual networks is called substrate
network.
Virtual Network Request (VNR)
Virtual Network Request is a complete single Virtual network
containing virtual nodes and virtual links.
Agent
In RL, an agent is the component that makes the decision abd take
action (i.e., embedding decision).
State
State is a representation (e.g., remaining SN capacity and
requested VN resource) of the current environment, and it tells
the agent what situation it is in currently.
Action
Actions (i.e., node and link embedding) are behavior an RL agent
can do to change the states of the environment.
Policy
A policy defines an agent's way of behaving at a given time. It
is a mapping from perceived states of environment to actions to be
taken when in those states. It is usually implemented as a deep
learning model because the state and action spaces are too large
to be completely known.
Reward
A reward is the feedback which provides an agent to the agent for
taking actions that lead to good outcomes (i.g., achieve the
objective of the network operator).
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Environment
An environment is the agent's world in which it lives and
interacts. The agent can interact with the environment by
performing some action but cannot influence the rules of the
environment by those actions.
4. Problem Space
RL contains three main components: state representation, action
space, and reward description. For solving a VNE problem, we need to
consider how to design the three main RL components. In addition, a
specific RL algorithm, training environment, sim2real gap, and
generalization are also important issues that should be considered
and addressed. We will describe each one in detail as follows.
4.1. State Representation
The way to understand and observe the VNE problem is crucial for an
RL agent to establish a thorough knowledge of the network status and
generate efficient embedding decisions. Therefore, it is essential
to firstly design the state representation that serves as the input
to the agent. The state representation is the information which an
agent can receive from the environment, and consists of a set of
values representing the current situation in the environment. Based
on the state representation, the RL agent selects the most
appropriate action through its policy model. In the VNE problem, an
RL agent needs to know the information of the overall SN entities and
their current status in order to use the resources of the nodes and
links of the substrate network. Also it must know the requirements
of the VNR. Therefore, in the VNE problem, the state usually should
represent the current resource state of the nodes and links of the
substrate network (ie, CPU, memory, storage, bandwidth, delay, loss
rate, etc.) and the requirements of the virtual node and link of the
VNR. The collected status information is used as raw input, or
refined status information through the feature extraction process is
used as input for the RL agent. The state representation may vary
depending on the operator's objective and VNE strategy. The method
of determining such feature extraction and representation greatly
affects the performance of the agent.
4.2. Action Space
In RL, an action represents a decision that an RL agent can take
based on current state representation. The set of all possible
actions is called an action space. In the VNE problems, actions are
generally divided into node embedding and link embedding. The action
for node embedding means the VNR's nodes are assigned to which nodes
in the SN. Also, for link embedding, the action represents the
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selected paths between the selected substrate network nodes from the
node embedding result. If the policy model of the RL agent is well
trained, it will select the embedding result to maximize the reward
appropriate for the operator's objectives. The output actions
generated from the agent will indicate the adjustment of allocated
resources. It is noted that, at each point of time step, an RL
algorithm may decide to 1) embed each virtual node onto substrate
nodes and then embed each virtual link onto substrate paths
separately, or 2) embed the given whole VNR onto substrate nodes and
links in the SN at once. In the former case, at every single step, a
learning agent focuses on exactly one virtual node from the current
VNR, and it generates a certain substrate node to host the virtual
node. Link embedding is then performed separately in the same time
step. To solve the VNE problem efficiently, mapping of virtual nodes
and links are considered together, although they are performed
separately. Link mapping is considering more complex than node
mapping, because a virtual link can be mapped onto a physical path
with different hops. On the other hand, at every single step, a
learning agent can try to embed the given whole VNR, i.e., all
virtual nodes and links in the given VNR, onto a subset of SN
components. The whole VNR embedding should be handled as a graph
embedding, so that the action space is huge and the design of the RL
algorithm is usually more difficult than the one with each node and
link embedding.
4.3. Reward Description
Designing rewards is an important issue for an RL algorithm. In
general, the reward is the benefit that an RL agent follows when
performing its determined action. Reward is an immediate value that
evaluates only the current state and action. The value of reward
depends on success or failure of each step. In order to select the
action that gives the best results in the long run, an RL agent needs
to select the action with the highest cumulative reward. The reward
is calculated through the reward function according to the objective
of the environment, and even in the same environment, it may be
different depending on the operator's objective. Based on the given
reward the agent can evaluate the effectiveness to improve the
policy. Hence, the reward function play a important rules in the
training process of RL. In the VNE problem, the overall objectives
are to reduce the VNE rejection, embed them with minimum cost,
maximize the revenue, and increase the resource utilization of
physical resources. Reward function should be designed to achieve
one or multiple ones of these objectives. Each objective and its
correspondent reward design are outlined as follows:
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Revenue
Revenue is the sum of the virtual resources requested by the VN,
and calculated to determine the total cost of the resources.
Typically, a successful action (e.g., VNR is embedded without
violation) is treated to be a good reward which also increases the
revenue. Otherwise, a failed action (e.g., VNR is rejected) leads
that the agent will receive a negative reward as well as
decreasing the revenue.
Cost
Cost is the expenditure incurred when VNR is embedded as a
substrate network. It's not a good embedding result to pursue
only high revenue. It is important for the network operator and
SP to spend less. The lower the cost, the better the agent will
be rewarded.
Acceptance Ratio
Acceptance ratio is the ratio measured by the number of
successfully embedded virtual network requests divided by total
number of virtual network requests. To achieve a high acceptance
ratio, the agent is trying to embed maximum VNR and get a good
reward. Getting a good reward is usually proportional to the
acceptance ratio.
Revenue-to-cost ratio
To balance and compare the cost of resources for embedding VNR,
the revenue is divided by cost. Revenue-to-cost ratio compares
the embedding algorithms with respect to their embedding results
in terms of the cost and revenue. Since most VNOs are most
interested in this objective, a reward function should be made to
relate to this performance metric.
4.4. Policy and RL Algorithms
The policy is the strategy that the agent employs to determine the
next action based on the current state. It maps states to actions
that promise the highest reward. Therefore, an RL agent updates its
policy repeatedly in the learning phase to maximize the expected
cumulative reward. Unlike supervised learning, in which each sample
has a corresponding label indicating the preferred output of the
learning model, an RL agent relies on reward signals to evaluate the
effectiveness of actions and further improve the policy. From the
perspective of RL, the goal of VNE is to find an optimal policy to
embed an VNR onto the given SN in any state at any time. There are
two types of RL algorithms: on-policy and off-policy. In on-policy
RL algorithms, the (behaviour) policy of the exploration step to
select an action and the policy to learn are the same. On-policy
algorithms work with a single policy, and require any observations
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(state, action, reward, next state) to have been generated using that
policy. Representative on-policy algorithms include A2C, A3C, TRPO,
and PPO. On the other hand, off-policy RL algorithms work with two
policies. These are a policy being learned, called the target
policy, and the policy being followed that generates the
observations, called the behaviour policy. In off-policy RL
algorithms, the learning policy and the behaviour policy are not
necessarily the same. It allows the use of exploratory policies for
collecting the experience, since learning and behavior policies are
separated. In the VNE problem, various experiences can be
accumulated by extracting embedding results using various behavior
policies. Representative off-policy algorithms include Q-learning,
DQN, DDPG, and SAC. There are different classifications for RL
algorithms: model-based and model-free. In model-based RL
algorithms, an RL agent learns its optimal behavior indirectly by
learning a model of the environment by taking actions and observing
the outcomes that include the next state and the immediate reward.
The models predict the outcomes of actions. The model is used
instead of the environment or in addition to interaction with it to
learn optimal policies. This becomes, however, impractical when the
state and action space is large. Unlike model-based algorithms,
model-free RL algorithms learn directly by trial and error with the
environment and do not require the relatively large memory. Since
data efficiency or safety is very important even in VNE problems, the
use of model-based algorithms can be actively considered. However,
since it is not easy to build a good model that mimics a real network
environment, a model-free RL algorithm may be more suitable for VNE
problems. In conclusion, a good RL algorithm selection plays an
important role in solving the VNE problem, and VNE performance
metrics vary depending on the selected RL algorithm.
4.5. Training Environment
Simulation is the use of software to simulate an interacting
environment that is difficult to actually execute and test. An RL
algorithm learns by iteratively interacting with the environment.
However, in the real environment, various variables such as failure
and component consumption exist. Therefore, it is necessary to learn
through a simulation that simulates the real environment. In order
to solve the VNE problem, we need to use a network simulator similar
to the real environment because it is difficult to repeatedly
experiment with real network environments using an RL algorithm, and
it is very challenging and overwhelming to directly apply an RL
algorithm to real-world environments. When solving VNE problems, a
network simulation environment similar to a real network is required.
The network simulation environment should have a general SN
environment and VNR required by the operator. The SN has nodes and
links between nodes, and each has capacity such as CPU and Bandwidth.
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In the case of VNR, there are virtual nodes and links required by the
operator, and each must have its own requirements.
As described in [DTwin2022], a digital twin network is a virtual
representation of the physical network environment and can be built
by applying digital twin technologies to the environment and creating
virtual images of diverse physical network facilities. The digital
twin for networks is an expansion platform of network simulation. In
[DTwin2022], Section 8.2 describes that a digital twin network
provides the complete machine learning lifecycle development by
providing a realistic network environment, including network
topologies, etc. Hence, RL algorithms to solve the VNE problem can
be trained and verified on a digital twin network upfront before
deployed to the physical networks, and the verification accuracy will
be generally high when the digital twin network reproduces network
behaviors well under various conditions. On the other hand, two
placeholders marked as [DTwin2022] in the above new paragraph should
be replaced with the right reference number after inserting the
following new Internet-Draft, which introduces the definition,
architecture, and use-cases of digital twin network, into "Section 7.
Informative References" of our Internet Draft.
4.6. Sim2Real Gap
Sim-to-real is a very comprehensive concept and applied in many
fields including robotics and classic machine vision tasks. An RL
algorithm iteratively learns through a simulation environment to
train a model of the desired policy. The trained model is then
applied to the real environment and/or tuned more for adapting to the
real one. However, when the trained model is applied in the
simulation to the real environment, sim2real gap problem arises.
Closing the gap between simulation and reality gap in terms of
actuation requires simulators to be more accurate, and to account for
variability in agent dynamics. Obviously, the simulation environment
does not match perfectly to the real environment which mostly fails
in the tuning process and gives poor performance in the model because
of the Sim2Real gap. The sim2real gap is caused by the difference
between the simulation and the real environment. It is because the
simulation environment cannot perfectly simulate the real
environment, and there are many variables in the real environment.
In a real network environment for VNE, the SN's nodes and links may
fail due to external factors, or capacity such as CPU may change
suddenly. In order to solve this problem, the simulation environment
should be more robust or the trained RL model should be generalized.
To reduce the gap between sim and real network environments we need
to train our model with an efficient and large number of VNR and keep
learning the agent not only depend on previous memorization.
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4.7. Generalization
Generalization refers to the trained model's ability to adapt
properly to previously unseen new observations. An RL algorithm
tries to learn a model that optimizes some objective with the purpose
of performing well on data that has never been seen by the model
during training. In terms of VNE problems, the generalization is a
measure of how the agent's policy model performs on predicting unseen
VNR. The RL agent not only has to memorize all the previous variance
of the VNR but also to learn and explore more possible variance. It
is important to have good and efficient training data for VNR with
good variance and train the model with all possible VNRs.
5. IANA Considerations
This memo includes no request to IANA.
All drafts are required to have an IANA considerations section (see
Guidelines for Writing an IANA Considerations Section in RFCs
[RFC5226] for a guide). If the draft does not require IANA to do
anything, the section contains an explicit statement that this is the
case (as above). If there are no requirements for IANA, the section
will be removed during conversion into an RFC by the RFC Editor.
6. Security Considerations
All drafts are required to have a security considerations section.
See RFC 3552 [RFC3552] for a guide.
7. Informative References
[ASNVT2020]
Sharif, Kashif., Li, Fan., Latif, Zohaib., Karim, MM., and
Sujit. Biswas, "A Survey of Network Virtualization
Techniques for Internet of Things using SND and NFV",
DOI 10.1145/3379444, April 2020,
<https://doi.org/10.1145/3379444>.
[CDVNE2020]
"A Continuous-Decision Virtual Network Embedding Scheme
Relying on Reinforcement Learning",
DOI 10.1109/TNSM.2020.2971543, February 2020,
<https://ieeexplore.ieee.org/document/8982091>.
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[DeepViNE2019]
Dolati, M., Hassanpour, S. B., Ghaderi, M., and A.
Khonsari, "DeepViNE: Virtual Network Embedding with Deep
Reinforcement Learning", BCP 72, RFC 3552,
DOI 10.1109/INFCOMW.2019.8845171, September 2019,
<https://ieeexplore.ieee.org/document/8845171>.
[DTwin2022]
Yang, H., Zhou, C., Duan, X., Lopez, D., Pastor, A., and
Q. Wu, "Digital Twin Network: Concepts and Reference
Architecture", DOI https://datatracker.ietf.org/doc/draft-
irtf-nmrg-network-digital-twin-arch/, March 2022,
<https://datatracker.ietf.org/doc/draft-irtf-nmrg-network-
digital-twin-arch/>.
[DVNEGCN2021]
Zhang, Peiying., Wang, Chao., Kumar, NeeraJ., Zhang,,
Weishan., and Lei. Liu, "Dynamic Virtual Network Embedding
Algorithm based on Graph Convolution Neural Network and
Reinforcement Learning", DOI 10.1109/JIOT.2021.3095094,
July 2021, <https://ieeexplore.ieee.org/document/9475485>.
[ENViNE2021]
ULLAH, IHSAN., Lim, Hyun-Kyo., and Youn-Hee. Han, "Ego
Network-Based Virtual Network Embedding Scheme for Revenue
Maximization", DOI 10.1109/ICAIIC51459.2021.9415185, April
2021, <https://ieeexplore.ieee.org/document/9415185>.
[MLCNM2018]
Ayoubi, Sara., Noura, Limam., Salahuddin, Mohammad.,
Shahriar, Nashid., Boutaba, NRaouf., Estrada-Solano,
Felipe., and Oscar. M. Caicedo, "Machine Learning for
Cognitive Network Management",
DOI 10.1109/MCOM.2018.1700560, January 2018,
<https://ieeexplore.ieee.org/document/8255757>.
[MOQL2018] "Multi-Objective Virtual Network Embedding Algorithm Based
on Q-learning and Curiosity-Driven",
DOI 10.1109/TETC.2018.2871549, June 2018, <https://jwcn-
eurasipjournals.springeropen.com/articles/10.1186/
s13638-018-1170-x>.
[MVNE2020] "Modeling on Virtual Network Embedding using Reinforcement
Learning", DOI 10.1002/cpe.6020, September 2020,
<https://doi.org/10.1002/cpe.6020>.
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[MVNNML2021]
Boutaba, Raouf., Shahriar, Nashid., A, Mohammad., and
Noura. Limam, "Managing Virtualized Networks and Services
with Machine Learning",
DOI 48b8fc73c1609d4632d7db5e67e373a62a3cc1f6, January
2021, <https://www.semanticscholar.org/paper/Managing-
Virtualized-Networks-and-Services-with-Boutaba-
Shahriar/48b8fc73c1609d4632d7db5e67e373a62a3cc1f6>.
[NeuroViNE2018]
"NeuroViNE: A Neural Preprocessor for Your Virtual Network
Embedding Algorithm", DOI 10.1109/INFOCOM.2018.8486263,
June 2018, <https://ieeexplore.ieee.org/document/8486263>.
[NFVDeep2019]
Xiao, Y., Zhang, Q., Liu, F., Wang, J., Zhao, M., Zhang,
Z., and J. Zhang, "NFVdeep: Adaptive Online Service
Function Chain Deployment with Deep Reinforcement
Learning", RFC 1129, DOI 10.1145/3326285.3329056, June
2019, <https://doi.org/10.1145/3326285.3329056>.
[NRRL2020] "Network Resource Allocation Strategy Based on Deep
Reinforcement Learning", DOI 10.1109/OJCS.2020.3000330,
June 2020, <https://ieeexplore.ieee.org/document/9109671>.
[PPRL2020] "A Privacy-Preserving Reinforcement Learning Algorithm for
Multi-Domain Virtual Network Embedding",
DOI 10.1109/TNSM.2020.2971543, September 2020,
<https://ieeexplore.ieee.org/document/8982091>.
[QLDC2019] "A Q-Learning-Based Approach for Virtual Network Embedding
in Data Center", DOI 10.1007/s00521-019-04376, July 2019,
<https://link.springer.com/article/10.1007/
s00521-019-04376-6>.
[QVNE2020] Yuan, Y., Tian, Z., Wang, C., Zheng, F., and Y. Lv, "A Q-
learning-Based Approach for Virtual Network Embedding in
Data Center", DOI 10.1007/s00521-019-04376-6, July 2020,
<https://link.springer.com/article/10.1007/
s00521-019-04376-6>.
[RDAM2018] "RDAM: A Reinforcement Learning Based Dynamic Attribute
Matrix Representation for Virtual Network Embedding",
DOI 10.1109/TETC.2018.2871549, September 2018,
<https://ieeexplore.ieee.org/document/8469054>.
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[RFC3552] Rescorla, E. and B. Korver, "Guidelines for Writing RFC
Text on Security Considerations", BCP 72, RFC 3552,
DOI 10.17487/RFC3552, July 2003,
<https://www.rfc-editor.org/info/rfc3552>.
[RFC5226] Narten, T. and H. Alvestrand, "Guidelines for Writing an
IANA Considerations Section in RFCs", RFC 5226,
DOI 10.17487/RFC5226, May 2008,
<https://www.rfc-editor.org/info/rfc5226>.
[RFC7364] Thomas, P.T., Eric, Y., David, A., Luyuan, A., Larry, A.,
and A. Maria Napierala, "Problem Statement: Overlays for
Network Virtualization", October 2015,
<https://https://datatracker.ietf.org/doc/rfc7364/>.
[RLVNEWSN2020]
"Reinforcement Learning for Virtual Network Embedding in
Wireless Sensor Networks",
DOI 10.1109/WiMob50308.2020.9253442, October 2020,
<https://ieeexplore.ieee.org/document/9253442>.
[SUR2018] Boutaba, Raouf., Salahuddin, Mohammad., Limam, Noura.,
Ayoubi, Sara., Shahriar, Nashid., Estrada-Solano, Felipe.,
and Oscar. M. Caicedo, "A Comprehensive survey on Machine
Learning for Networking: Evolution, Applications and
Research Opportunities", DOI 10.1186/s13174-018-0087-2,
June 2018, <https://link.springer.com/article/10.1186/
s13174-018-0087-2>.
[VNEGCN2020]
Yan, Z., Ge, J., Wu, Y., Li, L., and T. Li, "Automatic
Virtual Network Embedding: A Deep Reinforcement Learning
Approach With Graph Convolutional Networks", RFC 1129,
DOI 10.1109/JSAC.2020.2986662, April 2020,
<https://ieeexplore.ieee.org/document/9060910>.
[VNEQS2021]
Wang, Chao., Batth, Ranbir Singh., Zhang, Peiying., Aujla,
Gagangeet., Duan, Youxiang., and Lihua. Ren, "VNE Solution
for Network Differentiated QoS and Security Requirements:
From the Perspective of Deep Reinforcement Learning",
DOI 10.1007/s00607-020-00883-w, January 2021,
<https://link.springer.com/article/10.1007/
s00607-020-00883-w>.
[VNESURV2013]
Fischer, Fischer., Botero, Juan Felipe., Till Beck,
Michael;., Karim, MM., De Meer, Hermann., and Xavier.
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Hesselbach, "Virtual Network Embedding: A Survey",
DOI 10.1109/SURV.2013.013013.00155, April 2020,
<https://doi.org/10.1109/SURV.2013.013013.00155>.
[VNETD2019]
Wang, S., Bi, J., V.Vasilakos, A., and Q. Fan, "VNE-TD: A
Virtual Network Embedding Algorithm Based on Temporal-
Difference Learning", BCP 72, RFC 3552,
DOI 10.1016/j.comnet.2019.05.004, October 2019,
<https://doi.org/10.1016/j.comnet.2019.05.004>.
[VNFFG2020]
Anh Quang, P.T., Hadjadj-Aoul, Y., and A. Outtagarts,
"Evolutionary Actor-Multi-Critic Model for VNF-FG
Embedding", RFC 1129, DOI 10.1109/CCNC46108.2020.9045434,
January 2020, <https://www.rfc-editor.org/info/rfc2629>.
[ZTORCH2018]
Sciancalepore, V., Chen, X., Yousaf, F. Z., and X. Costa-
Perez, "Z-TORCH: An Automated NFV Orchestration and
Monitoring Solution", BCP 72, RFC 3552,
DOI 10.1109/TNSM.2018.2867827, August 2018,
<https://ieeexplore.ieee.org/document/8450000>.
Authors' Addresses
Ihsan Ullah
KOREATECH
1600, Chungjeol-ro, Byeongcheon-myeon, Dongnam-gu
Cheonan
Chungcheongnam-do
31253
Republic of Korea
Email: ihsan@koreatech.ac.kr
Youn-Hee Han
KOREATECH
1600, Chungjeol-ro, Byeongcheon-myeon, Dongnam-gu
Cheonan
Chungcheongnam-do
31253
Republic of Korea
Email: yhhan@koreatech.ac.kr
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TaeYeon Kim
ETRI
218 Gajeong-ro, Yuseong-gu
Daejeon
34129
Republic of Korea
Email: tykim@etri.re.kr
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