AI-Based Distributed Processing Automation in Digital Twin Network
draft-oh-nmrg-ai-adp-02
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| Document | Type | Active Internet-Draft (individual) | |
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| Authors | Oh Seokbeom , Yong-Geun Hong , Joo-Sang Youn , Hyunjeong Lee , Hyun-Kook Kahng | ||
| Last updated | 2024-07-08 | ||
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draft-oh-nmrg-ai-adp-02
Internet Research Task Force S-B. Oh
Internet-Draft KSA
Intended status: Informational Y-G. Hong
Expires: 9 January 2025 Daejeon University
J-S. Youn
DONG-EUI University
HJ. Lee
ETRI
H-K. Kahng
Korea University
8 July 2024
AI-Based Distributed Processing Automation in Digital Twin Network
draft-oh-nmrg-ai-adp-02
Abstract
This document discusses the use of AI technology and digital twin
technology to automate the management of computer network resources
distributed across different locations. Digital twin technology
involves creating a virtual model of real-world physical objects or
processes, which is utilized to analyze and optimize complex systems.
In a digital twin network, AI-based network management by automating
distributed processing involves utilizing deep learning algorithms to
analyze network traffic, identify potential issues, and take
proactive measures to prevent or mitigate those issues. Network
administrators can efficiently manage and optimize their networks,
thereby improving network performance and reliability. AI-based
network management, utilizing digital twin network technology, also
aids in optimizing network performance by identifying bottlenecks in
the network and automatically adjusting network settings to enhance
throughput and reduce latency. By implementing AI-based network
management through automated distributed processing, organizations
can improve network performance, and reduce the need for manual
network management tasks.
Status of This Memo
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 3
2. Conventional Task Distributed Processing Techniques and
Problems . . . . . . . . . . . . . . . . . . . . . . . . 3
2.1. Challenges and Alternatives in Task Distributed
Processing . . . . . . . . . . . . . . . . . . . . . . . 3
2.2. Considerations for Resource Allocation in Task Distributed
Processing . . . . . . . . . . . . . . . . . . . . . . . 7
3. Requirements of Conventional Task Distributed Processing . . 8
4. Automating Distributed Processing with Digital Twin and AI . 8
5. Technologies for AI-Based Distributed Processing Automation in
Digital Twin Network . . . . . . . . . . . . . . . . . . 9
5.1. Configuration of Digital Twin Network . . . . . . . . . . 9
5.2. Data Collection and Processing . . . . . . . . . . . . . 10
5.3. AI Model Training and Deployment . . . . . . . . . . . . 10
5.4. AI-based Distributed Processing . . . . . . . . . . . . . 10
6. Security Considerations . . . . . . . . . . . . . . . . . . . 10
6.1. Data Validation and Bias Mitigation . . . . . . . . . . . 10
6.2. AI Model Vulnerability Detection . . . . . . . . . . . . 11
7. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 11
8. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . 11
9. Informative References . . . . . . . . . . . . . . . . . . . 11
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 12
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1. Introduction
Due to industrial digitalization, the number of devices connected to
the network is increasing rapidly. As the number of devices
increases, the amount of data that needs to be processed in the
network is increasing due to the interconnection between various
devices.
Existing network management was managed manually by administrators/
operators, but network management becomes complicated, and the
possibility of network malfunction increases, which can cause serious
damage.
Digital twin is a digital representation of an object of interest and
may require different capabilities (e.g., synchronization, real-time
support) according to the specific domain of application [Y.4600].
Digital twin systems help organizations improve important functional
objectives including real-time control, off-line analytics,
predictive maintenance by modelling and simulating of objects in the
real world. Therefore, it is important for a digital twin system to
represent as much real-world information about the object as possible
when digitally representing the object.
Therefore, this document considers the configuration of systems using
both digital twin technology and artificial intelligence (AI)
technology for network management and operation, in order to adapt to
the dynamically changing network environment. In this regard, AI
technologies play a key role by maximizing the utilization of network
resources. They achieve this by providing resource access control
and optimal task distribution processing based on the characteristics
of nodes that offer network functions for network management
automation and operation[I-D.irtf-nmrg-ai-challenges].
2. Conventional Task Distributed Processing Techniques and Problems
2.1. Challenges and Alternatives in Task Distributed Processing
Conventional Task Distributed Processing Techniques refer to methods
and approaches used to distribute computational tasks among multiple
nodes in a network. These techniques are typically used in
distributed computing environments to improve the efficiency and
speed of processing large volumes of data.
Some common conventional techniques used in task distributed
processing include load balancing, parallel processing, and
pipelining. Load balancing involves distributing tasks across
multiple nodes in a way that minimizes the overall workload of each
node, while parallel processing involves dividing a single task into
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multiple sub-tasks that can be processed simultaneously. Pipelining
involves breaking a task into smaller stages, with each stage being
processed by a different node.
However, conventional task distributed processing techniques also
face several challenges and problems. One of the main challenges is
ensuring that tasks are distributed evenly among nodes, so that no
single node is overburdened while others remain idle. Another
challenge is managing the communication between nodes, as this can
often be a bottleneck that slows down overall processing speed.
Additionally, fault tolerance and reliability can be problematic, as
a single node failure can disrupt the entire processing workflow.
To address these challenges, new techniques such as edge computing,
and distributed deep learning are being developed and used in modern
distributed computing environments. The optimal resource must be
allocated according to the characteristics of the node that provides
the network function. Cloud servers generally have more powerful
performance. However, to transfer data from the local machine to the
cloud, it is necessary to move across multiple access networks, and
it takes high latency and energy consumption because it processes and
delivers a large number of packets. The MEC server is less powerful
and less efficient than the cloud server, but it can be more
efficient considering the overall delay and energy consumption
because it is placed closer to the local machine[MEC.IEG006]. These
architectures combine computing energy, telecommunications, storage,
and energy resources flexibly, requiring service requests to be
handled in consideration of various performance trade-offs.
The existing distributed processing technique can divide the case
according to the subject performing the service request as follows.
(1) All tasks are performed on the local machine.
Local Machine
+-------------------+
| Perform all tasks |
| on local machine |
| |
| +---------+ |
| | | |
| | | |
| | | |
| | | |
| +---------+ |
| Local |
+-------------------+
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Figure 1: All tasks on local machine
(2) Some of the tasks are performed on the local machine and some are
performed on the MEC server.
Local Machine MEC Server
+-------------------+ +-------------------+
| Perform tasks | | Perform tasks |
| on local machine | | on MEC server |
| | | |
| +---------+ | | +-------------+ |
| | | | | | | |
| | | | | | | |
| | | | | | | |
| | | | | | | |
| +---------+ | | +-------------+ |
| Local | | MEC |
+-------------------+ +-------------------+
Figure 2: Some tasks on local machine and MEC server
(3) Some of the tasks are performed on local machine and some are
performed on cloud server
Local Machine Cloud Server
+-------------------+ +-------------------+
| Perform tasks | | Perform tasks |
| on local machine | | on cloud server |
| | | |
| +---------+ | | +-------------+ |
| | | | | | | |
| | | | | | | |
| | | | | | | |
| | | | | | | |
| +---------+ | | +-------------+ |
| Local | | Cloud |
+-------------------+ +-------------------+
Figure 3: Some tasks on local machine and cloud server
(4) Some of the tasks are performed on local machine, some on MEC
servers, some on cloud servers
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Local Machine MEC Server Cloud Server
+-------------------+ +-------------------+ +-------------------+
| Perform tasks | | Perform tasks | | Perform tasks |
| on local machine | | on MEC server | | on cloud server |
| | | | | |
| +---------+ | | +-------------+ | | +-------------+ |
| | | | | | | | | | | |
| | | | | | | | | | | |
| | | | | | | | | | | |
| | | | | | | | | | | |
| +---------+ | | +-------------+ | | +-------------+ |
| Local | | MEC | | Cloud |
+-------------------+ +-------------------+ +-------------------+
Figure 4: Some tasks on local machine, MEC server, and cloud server
(5) Some of the tasks are performed on the MEC server and some are
performed on the cloud server
MEC Server Cloud Server
+-------------------+ +-------------------+
| Perform tasks | | Perform tasks |
| on MEC server | | on cloud server |
| | | |
| +---------+ | | +-------------+ |
| | | | | | | |
| | | | | | | |
| | | | | | | |
| | | | | | | |
| +---------+ | | +-------------+ |
| MEC | | Cloud |
+-------------------+ +-------------------+
Figure 5: Some tasks on MEC server and cloud server
(6) All tasks are performed on the MEC server
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MEC Server
+-------------------+
| Perform all tasks |
| on MEC server |
| |
| +---------+ |
| | | |
| | | |
| | | |
| | | |
| +---------+ |
| MEC |
+-------------------+
Figure 6: All tasks on MEC server
(7) All tasks are performed on cloud servers
Cloud Server
+-------------------+
| Perform all tasks |
| on cloud server |
| |
| +---------+ |
| | | |
| | | |
| | | |
| | | |
| +---------+ |
| Cloud |
+-------------------+
Figure 7: All tasks on cloud server
2.2. Considerations for Resource Allocation in Task Distributed
Processing
In addition, it is necessary to consider various environments
depending on the delay time and the importance of energy consumption
to determine which source is appropriate to handle requests for
resource use. The importance of delay time and energy consumption
depends on the service requirements for resource use. There is a
need to adjust the traffic flow according to service requirements.
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3. Requirements of Conventional Task Distributed Processing
The requirements of task distributed processing refer to the key
elements that must be considered and met to effectively distribute
computing tasks across multiple nodes in a network. These
requirements include:
* Scalability: The ability to add or remove nodes from the network
and distribute tasks efficiently and effectively, without
compromising performance or functionality.
* Fault tolerance: The ability to handle node failures and network
outages without disrupting overall system performance or task
completion.
* Load balancing: The ability to distribute tasks evenly across all
nodes, ensuring that no single node becomes overwhelmed or
underutilized.
* Task coordination: The ability to manage task dependencies and
ensure that tasks are completed in the correct order and on time.
* Resource management: The ability to manage system resources such
as memory, storage, and processing power effectively, to optimize
task completion and minimize delays or errors.
* Security: The ability to ensure the integrity and confidentiality
of data and tasks, and protect against unauthorized access or
tampering.
Meeting these requirements is essential to the successful
implementation and operation of task distributed processing systems.
The effective distribution of tasks across multiple nodes in a
network can improve overall system performance and efficiency, while
also increasing fault tolerance and scalability.
4. Automating Distributed Processing with Digital Twin and AI
Automating distributed processing utilizing digital twin technology
involves digitally modeling physical objects and processes from the
real world, enabling real-time tracking and manipulation. This
technology enables real-time monitoring and manipulation,
revolutionizing how we understand and manage complex networks.
When combined with AI technology, these digital twins form a robust
automated distributed processing system. For instance, digital twins
can project all nodes and devices within a network digitally, The AI
model can utilize various types of information, such as:
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* Network data: Network-related data such as network traffic, packet
loss, latency, bandwidth usage, etc., can be valuable for
distributed processing automation. This data helps in
understanding the current state and trends of the network,
optimizing task distribution, and processing.
* Task and task characteristic data: Data that describes the
characteristics and requirements of the tasks processed in the
distributed processing system is also important. This can include
the size, complexity, priority, dependencies, and other attributes
of the tasks. Such data allows the AI technology to distribute
tasks appropriately and allocate them to the optimal nodes.
* Performance and resource data: Data related to the performance and
resource usage of the distributed processing system is crucial.
For example, data representing the processing capabilities of
nodes, memory usage, bandwidth, etc., can be utilized to
efficiently distribute tasks and optimize task processing.
* Network configuration and device data: External environmental
factors should also be considered. Data such as network topology,
connectivity between nodes, energy consumption, temperature, etc.,
can be useful for optimizing task distribution and processing.
AI algorithms, based on this digital twin data, can automatically
optimize network operations. For example, if overload is detected on
a specific node, AI can redistribute tasks to other nodes, minimizing
congestion. The real-time updates from digital twins enable
continuous, optimal task distribution, allowing the network to adapt
swiftly to changes.
By integrating digital twins and AI, the automated distributed
processing system maximizes network performance while minimizing
bottlenecks. This technology reduces the burden on network
administrators, eliminating the need for manual adjustments and
enhancing network flexibility and responsiveness.
5. Technologies for AI-Based Distributed Processing Automation in
Digital Twin Network
5.1. Configuration of Digital Twin Network
In a network environment, digital twins are used to monitor the
performance of the network infrastructure in real-time, optimize
network traffic through AI-based distributed processing, predict
issues, and automatically resolve them. To this end, it is important
to select physical objects to be represented as digital twins in
order to collect the various data described in Section 4.
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5.2. Data Collection and Processing
Monitoring agents installed on network devices collect real-time
data. This data includes traffic volume, latency, packet loss rates,
CPU and memory usage, etc. Edge computing devices perform initial
data processing before transmitting the data to the central
management system.
5.3. AI Model Training and Deployment
The central system trains models for traffic prediction, fault
prediction, and optimization based on the collected data. The
trained models are deployed to network devices to perform real-time
traffic analysis and optimization tasks.
5.4. AI-based Distributed Processing
Each network device or edge computing device analyzes data in real-
time and dynamically adjusts traffic routes. The overall network
status is monitored, and in case of a fault, traffic is automatically
rerouted or devices are reset. Distributed edge devices communicate
with each other to share network status and collaborate with the
central system to optimize the entire network.
6. Security Considerations
When providing AI services, it is essential to consider security
measures to protect sensitive data such as network configurations,
user information, and traffic patterns. Robust privacy measures must
be in place to prevent unauthorized access and data breaches.
Implementing effective access control mechanisms is essential to
ensure that only authorized personnel or systems can access and
modify the network management infrastructure. This involves managing
user privileges, using authentication mechanisms, and enforcing
strong password policies.
6.1. Data Validation and Bias Mitigation
Ensuring the quality and integrity of the training data is critical
for AI model performance. This involves several key steps:
* Data Validation Procedures: Implement rigorous validation
processes, including data cleaning to remove noise and irrelevant
data, consistency checks to ensure uniformity across datasets, and
anomaly detection to address outliers that could skew model
training.
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* Bias Detection and Mitigation: Ensure fairness and accuracy by
using diverse data sources, applying fairness metrics, and
performing adversarial testing to identify and mitigate biases.
6.2. AI Model Vulnerability Detection
Regularly auditing and evaluating the AI model is essential to detect
and address vulnerabilities:
* Performance Monitoring: Continuously monitor the AI model's
performance to identify any degradation or unexpected behavior.
* Security Testing: Conduct security tests such as penetration
testing and adversarial attacks to evaluate the model's
robustness.
* Update and Patch Management: Keep the AI model and its underlying
systems updated with the latest security patches and improvements.
Enhancing the explainability and transparency of AI models is also
important:
* Model Interpretability Tools: Use tools and techniques to
interpret the AI model's decisions and understand the factors
influencing its predictions.
* Transparent Reporting: Provide clear and transparent reports on
the AI model's performance, biases, and decision-making processes
to stakeholders.
7. IANA Considerations
There are no IANA considerations related to this document.
8. Acknowledgements
TBA
9. Informative References
[Y.4600] Union, I. T., ""Recommendation ITU-T Y.4600 (2022),
Requirements and capabilities of a digital twin system for
smart cities.", August 2022.
[I-D.irtf-nmrg-ai-challenges]
François, J., Clemm, A., Papadimitriou, D., Fernandes, S.,
and S. Schneider, "Research Challenges in Coupling
Artificial Intelligence and Network Management", Work in
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Progress, Internet-Draft, draft-irtf-nmrg-ai-challenges-
03, 4 March 2024, <https://datatracker.ietf.org/doc/html/
draft-irtf-nmrg-ai-challenges-03>.
[MEC.IEG006]
ETSI, "Mobile Edge Computing; Market Acceleration; MEC
Metrics Best Practice and Guidelines", Group
Specification ETSI GS MEC-IEG 006 V1.1.1 (2017-01),
January 2017.
Authors' Addresses
SeokBeom Oh
KSA
Digital Transformation Center, 5
Teheran-ro 69-gil, Gangnamgu
Seoul
06160
South Korea
Phone: +82 2 1670 6009
Email: isb6655@korea.ac.kr
Yong-Geun Hong
Daejeon University
62 Daehak-ro, Dong-gu
Daejeon
34520
South Korea
Phone: +82 42 280 4841
Email: yonggeun.hong@gmail.com
Joo-Sang Youn
DONG-EUI University
176 Eomgwangno Busan_jin_gu
Busan
614-714
South Korea
Phone: +82 51 890 1993
Email: joosang.youn@gmail.com
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Hyunjeong Lee
Electronics and Telecommunications Research Institute
218 Gajeong-ro, Yuseong-gu
Daejeon
34129
South Korea
Phone: +82 42 860 1213
Email: hjlee294@etri.re.kr
Hyun-Kook Kahng
Korea University
2511 Sejong-ro
Sejong City
Email: kahng@korea.ac.kr
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