Network management by automating distributed processing based on artificial intelligence
draft-oh-nmrg-ai-adp-00
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draft-oh-nmrg-ai-adp-00
Internet Research Task Force S-B. Oh
Internet-Draft KSA
Intended status: Informational Y-G. Hong
Expires: 11 January 2024 Daejeon University
J-S. Youn
DONG-EUI University
H-K. Kahng
Korea University
10 July 2023
Network management by automating distributed processing based on
artificial intelligence
draft-oh-nmrg-ai-adp-00
Abstract
This document discusses the use of AI technology to automate the
management of computer network resources distributed across different
locations. 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
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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provisions of BCP 78 and BCP 79.
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material or to cite them other than as "work in progress."
This Internet-Draft will expire on 11 January 2024.
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Copyright Notice
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
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 using Artificial
Intelligence . . . . . . . . . . . . . . . . . . . . . . 8
5. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 10
6. Security Considerations . . . . . . . . . . . . . . . . . . . 10
7. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . 10
8. Informative References . . . . . . . . . . . . . . . . . . . 10
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 11
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.
Therefore, this document considers the configuration of systems using
artificial intelligence (AI) technology for network management and
operation, in order to adapt to the dynamically changing network
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environment. In this regard, AI technologies maximize the
utilization of network resources 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
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.
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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 |
+-------------------+
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.
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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 using Artificial Intelligence
Automating distributed processing using AI refers to the use of AI
technologies, such as machine learning and deep learning, to automate
the distribution and processing of tasks across a network.
In traditional distributed processing systems, tasks are distributed
manually or based on predetermined rules, which can lead to
inefficiencies and suboptimal performance. However, by leveraging AI
technologies, distributed processing can be automated in a way that
maximizes performance and minimizes delays or bottlenecks.
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AI algorithms can analyze network conditions and user demand in real-
time, allowing for dynamic task distribution and processing based on
current network conditions. For example, an AI-based distributed
processing system might use machine learning algorithms to analyze
network traffic patterns and identify areas of congestion or
bottlenecks. The system could then automatically reroute tasks to
less congested areas of the network, reducing delays and improving
overall performance.
In addition to optimizing task distribution, AI can also be used to
optimize task processing. For example, AI algorithms can analyze the
characteristics of individual tasks and distribute them to nodes in
the network that are best suited to handle them, based on factors
such as processing power or available memory. This can improve
processing efficiency and reduce processing times.
Overall, automating distributed processing using AI can improve
network performance, reduce delays, and increase efficiency, making
it a valuable tool for network management and operations.
To automate distributed processing using AI technology, various types
of data can be used as training data. Here are some common data
types:
* 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.
* Environmental 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.
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This data can be collected from real network environments and used
for training AI through appropriate data collection and preprocessing
processes.
5. IANA Considerations
There are no IANA considerations related to this document.
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.
Maintaining the security and integrity of the training data used for
AI models is vital. It is important to ensure that the training data
is unbiased, representative, and free from malicious content or data
poisoning. This is crucial for the accuracy and reliability of the
AI models.
7. Acknowledgements
TBA
8. Informative References
[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
Progress, Internet-Draft, draft-irtf-nmrg-ai-challenges-
00, 10 May 2023, <https://datatracker.ietf.org/doc/html/
draft-irtf-nmrg-ai-challenges-00>.
[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.
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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
Hyun-Kook Kahng
Korea University
2511 Sejong-ro
Sejong City
Email: kahng@korea.ac.kr
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