AI based Network Management Agent(NMA): Concepts and Architecture
draft-zhao-nmop-network-management-agent-00
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| Document | Type | Active Internet-Draft (individual) | |
|---|---|---|---|
| Authors | XingZhao , Yunbin Xu , Chaode Yu , Haijun Meng , Yipeng Fu | ||
| Last updated | 2024-10-20 | ||
| RFC stream | (None) | ||
| Intended RFC status | (None) | ||
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| Consensus boilerplate | Unknown | ||
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draft-zhao-nmop-network-management-agent-00
Network Management Operations X. Zhao
Internet-Draft Y. Xu
Intended status: Informational CAICT
Expires: 23 April 2025 C. Yu
Huawei
H. Meng
Y. Fu
CAICT
20 October 2024
AI based Network Management Agent(NMA): Concepts and Architecture
draft-zhao-nmop-network-management-agent-00
Abstract
With the development of AI(Artificial Intelligence) technology, large
model have shown significant advantages and great potential in
recognition, understanding, decision-making, and generation, and can
well match the self-intelligent network management requirements for
the goal of autonomous network[TMF-IG1230] or Intent-based Networking
[RFC9315], and can be used as one of the potential driving
technologies to drive high-level autonomous networks. When
introducing AI for network management, how to integrate AI technology
and deal with the relationship with the existing network management
entity (such as network controller) is the focus of research and
standardization.
This document presents the concept of AI based network management
agent(NMA), provides the basic definition and reference architecture
of NMA, discusses the relationship of NMA with traditional network
controller or other network management entity by exploring the
delpoyment mode of NMA, and proposes the comman processing flow and
typical application scenarios of NMA.
Discussion Venues
This note is to be removed before publishing as an RFC.
Discussion of this document takes place on the Network Management
Operations Working Group mailing list (nmop@ietf.org), which is
archived at https://mailarchive.ietf.org/arch/browse/nmop/.
Source for this draft and an issue tracker can be found at
https://github.com/ietf-wg-nmop/draft-ietf-nmop-digital-map-concept.
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Status of This Memo
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This Internet-Draft will expire on 23 April 2025.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 3
2. Terminology . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.1. Acronyms and Abbreviations . . . . . . . . . . . . . . . 4
2.2. Definitions . . . . . . . . . . . . . . . . . . . . . . . 4
3. Reference architecture of AI based network management
agent(NMA) . . . . . . . . . . . . . . . . . . . . . . . 4
3.1. Function Architecture of NMA . . . . . . . . . . . . . . 5
3.1.1. Base Layer . . . . . . . . . . . . . . . . . . . . . 6
3.1.2. Instance Layer . . . . . . . . . . . . . . . . . . . 6
3.2. Deployment mode of NMA . . . . . . . . . . . . . . . . . 8
4. Common processing flow of NMA . . . . . . . . . . . . . . . . 9
5. Related Interfaces . . . . . . . . . . . . . . . . . . . . . 11
6. Typical application scenarios after introducing NMA . . . . . 11
7. Security Considerations . . . . . . . . . . . . . . . . . . . 12
8. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 12
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9. References . . . . . . . . . . . . . . . . . . . . . . . . . 12
9.1. Normative References . . . . . . . . . . . . . . . . . . 12
9.2. Informative References . . . . . . . . . . . . . . . . . 12
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 13
1. Introduction
As the types of operator services become increasingly diverse, the
complexity and difficulty of network operations and maintenance
continue to grow. On one hand, new service scenarios such as
industrial internet, vehicle-road collaboration, and 5GtoB for
vertical industries are constantly emerging, and customer services
like Extended Reality (XR), Virtual Reality (VR), and smart home are
becoming more abundant, with a continuous increase in network access
volume. On the other hand, with the popularization of 5G and gigabit
optical networks, operators' networks are facing a situation where
networks from 2G to 5G coexist. The network protocols and
characteristics vary across different network domains, leading to a
continuous increase in the difficulty and complexity of network
operations and maintenance. Relying solely on traditional manual
operations and maintenance methods can no longer meet the
increasingly complex network operations and maintenance demands. The
level of network intelligence has become a key factor directly
affecting network performance and user experience. Against this
backdrop, enhancing the level of network intelligence and creating
Autonomous Networks (AN)[TMF-IG1230] has become a global consensus
among operators, with mainstream operators releasing goals and plans
to achieve Level 4 (L4) autonomous networks by 2025.
L4+ AN sets higher requirement in intention, decision-making,
analysis, perception, and execution. Artificial Intelligence (AI)
large model technology has shown significant advantages and great
potential in identification, understanding, decision-making, and
generation. It has technical features such as multimodal fusion
perception capabilities, more user-friendly human-computer
interaction and knowledge Q&A capabilities, and content generation
capabilities, which can well match the new requirements of Level 4
Autonomous Networks and already be one of the core driving
technologies to achieve high-level autonomous networks.
The application and deployment methods of AI after its introduction
are still unclear, as well as the relationship between AI and the
existing network management and control systems, and in what form it
can help network management, which are key issues that need to be
discussed currently.
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Therefore, this document proposes the concept of AI-based network
management agent (NMA), defines the reference architecture of NMA,
and discusses the application mode, general task processing flow,
interface requirements, and typical application scenarios of NMA.
2. Terminology
2.1. Acronyms and Abbreviations
AI: Artificial Intelligence
LLM: Large Language Model
NMA: Network Management Agent, refers to AI based network management
agent
2.2. Definitions
The document defines the following terms:
Network Management Agent (NMA): A network management entity with
autonomous task processing capabilities, which is encapsulated
based on AI algorithm or AI model, and has task intent [RFC9315]
perception, planning, decision-making, and execution capabilities.
It can understand the input operation intent through AI model,
call other functional components of the control system or external
interfaces to complete task processing, and return processing
results. For different application scenarios, NMA can be
subdivided into multiple scenario-oriented agents.
NMA Instance: The instantiated agent applications which can
automatically perform certain network management tasks for
specific network management scenarios. For different application
scenarios, there can be multiple scenario-oriented agent instances
(like apps in the phone), which can be called “NMA instance” for
short in this document.
3. Reference architecture of AI based network management agent(NMA)
The network management agent (NMA) is a new network management entity
with autonomous task processing capabilities, which is encapsulated
based on AI algorithms or AI models, and has task intent perception,
task planning, decision-making, and execution capabilities. It can
understand the input operation intent through AI models, call other
functional components of the control system or external interfaces to
complete task processing, and return processing results
automatically.
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3.1. Function Architecture of NMA
Based on the traditional AI agent concepts and frameworks, this
document presents the architecture of AI agent for network management
as shown in Figure 1.
+-----------------------------------------------------------------+
| AI based network management agent(NMA) |
| |
| +------------------------Instance layer-----------------------+ |
| | +-------------------------+ +-------------------------+ | |
| | | Agent Instances | | | | |
| | | (NMA instance) | | | | |
| | | +---------------------+ | | | | |
| | | | Fault Treatment | | | | | |
| | | +---------------------+ | | | | |
| | | +---------------------+ | | NMA | | |
| | | | Network Planning | | | Instance | | |
| | | +---------------------+ |<--->| Management | | |
| | | +---------------------+ | | | | |
| | | | Network Optimization| | | | | |
| | | +---------------------+ | | | | |
| | | +---------------------+ | | | | |
| | | | ...... | | | | | |
| | | +---------------------+ | | | | |
| | +-------------------------+ +-------------------------+ | |
| +------------------------------^------------------------------+ |
| | |
| v |
| +-------------------------Base layer--------------------------+ |
| | +-------------------------+ +-------------------------+ | |
| | | AI based Basic services | | Knowledge and memory | | |
| | | +---------------------+ | | +---------------------+ | | |
| | | | Intent Management | | | | Knowledge Base | | | |
| | | +---------------------+ | | +---------------------+ | | |
| | | +---------------------+ | | +---------------------+ | | |
| | | | Task Planning | | | | Knowledge Retrieval | | | |
| | | +---------------------+ |<--->| +---------------------+ | | |
| | | +---------------------+ | | +---------------------+ | | |
| | | | Task Execution | | | | Memory Management | | | |
| | | +---------------------+ | | +---------------------+ | | |
| | | +---------------------+ | | +---------------------+ | | |
| | | | Tool Invocation | | | | Memory Retrieval | | | |
| | | +---------------------+ | | +---------------------+ | | |
| | +-------------------------+ +-------------------------+ | |
| +-------------------------------------------------------------+ |
+-----------------------------------------------------------------+
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Figure 1: Key Elements of AI based network management agent (NMA)
The NMA consists of two main layers, the specific components and
functions of each layer are as follows:
3.1.1. Base Layer
Base Layer includes AI based basic services as well as knowledge and
memory subsystem.
AI based Basic Services: Provide a unified intelligent agent engine
framework, build interactive intelligence public capabilities,
simplify application development, integrate Large Language Models
(LLM), knowledge retrieval, API invocations, etc., to achieve the
full process orchestration from intent understanding, task
planning, tool invocation to task execution.
Knowledge and Memory Subsystem: Provides unified search for local
multi-type knowledge bases (vector knowledge base, system online
help, operation and maintenance data logs), combines LLM to
complete knowledge fusion and extraction, and improves the
accuracy of downstream tasks (knowledge Q&A/task planning, etc.).
Realizes knowledge injection and integrated retrieval. Among
them, the knowledge base and knowledge retrieval capabilities can
be deployed inside or outside the NMA according to actual needs.
3.1.2. Instance Layer
Instance Layer includes agent instances and instance management
functions.
Agent Instances: It refers to the instantiated agent applications
which can automatically perform certain network management tasks
for specific network management scenarios. When an agent instance
receives a request from the user, it can leverage the capabilities
of the base layer to address complex tasks in various network
operational and maintenance scenarios. It achieves understanding
of task intent, plans and decomposes sub-goals, acquires and
distributes information, and flexibly schedules AI models as well
as invoke related function APIs to complete the execution of
specific tasks, and then feeds the execution results back to the
users.
For different application scenarios, there can be multiple
scenario-oriented agent instances (like apps in the phone), which
can be called “NMA instance” for short in this document. Aimed at
the network planning, construction, maintenance, optimization, and
operation scenarios, the main NMA instances could include:
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* Network Fault Handling Instance: This instance can be created
by pre-training specific AI model based on the network
troubleshooting guidance documents, network equipment product
documents, and other materials. The instance can solidify the
fault handling experience of experts, and realize fault impact
analysis, root cause self-diagnosis, and self-repair of network
faults by orchestrating and calling models or network control
APIs. It also interfaces with the list dispatching system to
achieve list self-closed loop, etc.
* Network Planning Instance: The instance can make use of the
capabilities of AI large model to understand the network
planning intent (user intent, business development goals,
network construction plans, etc.) through LLM technology, and
analyzes and forecasts the current network resource usage
(traffic, performance, user scale, resource utilization, etc.)
to output planning schemes.
* Network Optimization Instance: Understands the network
optimization goal through natural language, converts the
optimization intent into network optimization constraint rules,
such as network load thresholds, service route optimization
strategies, etc. The instance can use traffic prediction
models to predict the future traffic and bandwidth utilization
of the entire network, automatically generate resource, hidden
danger, performance, traffic, and other prediction results, and
can automatically generate optimization strategies based on the
prediction results to perform traffic pre-diversion, autonomous
decision-making, and automatic execution to achieve dynamic
energy saving of equipment and optimal traffic of the entire
network, etc.
* Intelligent Assistant Instance: This instance can have open Q&A
capability based on LLM, providing a dialogue Q&A style
operation and maintenance. Users can "one-click" input fault
descriptions or resource names in natural language, and the
instance will automatically perform intent recognition and
query to significantly improve the efficiency of knowledge
questioning, fault reporting, and maintenance support.
Instance Management: Implements basic management capabilities such
as registration of intelligent agent instances, lifecycle
management, operation monitoring, and log auditing. It also
provides manual takeover switch control capabilities, providing
platform support for intelligent agent collaboration and
integrated evolution.
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3.2. Deployment mode of NMA
The NMA can be part of existing SDN-based network controller or be an
independent system. Correspondingly, there can be two deployment
modes between the NMA and the original network control system (or
controller), as shown in Figure 2.
+-----------------------------+ +--------------------+
| | | |
| Original Network Management <-MCS_A_I-> Network Management |
| and Control System(MCS) | | Agent(NMA) |
| | | |
+--------------^--------------+ +----------^---------+
| |
Southbound Interface(SBI) Intelligent SBI(I_SBI)
| |
+--------------v-----------------------------------v---------+
| Physical Network |
+------------------------------------------------------------+
(a)
+------------------------------------------------------------+
| Network Management and Control System(MCS) |
| |
| +--------------------+ +--------------------+ |
| | Original Function <--Internal-> Network management | |
| | Modules | Interface | Agent(NMA) | |
| +--------------------+ (I_I) +--------------------+ |
| |
+------------------------------^-----------------------------+
|
Extended SBI(E_SBI)
|
+------------------------------v-----------------------------+
| Physical Network |
+------------------------------------------------------------+
(b)
Figure 2: Deployment mode of network management agent (NMA)
Independent deployment mode: As shown in Figure 2(a), NMA is
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independently deployed from the original network management and
control system (MCS). NMA and MCS are independent systems. A new
east-west interface needs to be added between NMA and MCS to
achieve capability calling and result feedback operations. This
interface can be called “MCS_A_I”. In this deployment mode, MCS
use southbound interface (SBI) to interact with physical network,
while an intelligent southbound interface (abbreviated as “I_SBI”)
needs to be added between NMA and the underlying physical network.
Integrated deployment mode: As shown in Figure 2(b), NMA is
integrated and deployed with the original network management and
control system (MCS), and the NMA serves as a function of MCS.
NMA interacts with original function modules through internal
interface (abbreviated as “I_I”). The enhanced MCS interacts with
underlay physical network through extended SBI (abbreviated as
“E_SBI”).
The specific functional requirements and information model
definition of interfaces mentioned above will be discussed in the
following version.
4. Common processing flow of NMA
The embedded AI model within NMA serves as the interface for user
information input, and NMA instance uses the large model as the
interface to clarify problems through multiple rounds, analyze
positioning, generate plans, invoke interfaces/tools to handle
problems, and complete closed-loop processing of problems, so as to
build end-to-end problem processing assistance capabilities.
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User/Network
+-----> Management Task
| |
| v
| Intent Analysis <-------+ +-- Service Configuration
| | | | API/Tool
| | v |
| | Model Reasoning | Alarm Monitor
| | ^ | API/Tool
| v | |
| Task Decomposition <----+ | Performance Monitor
| | | API/Tool
| v |
| Tool/API Invocation-----> Toolkit ----+ Network Optimization
| | | ^ | API/Tool
| v | | |
| Process Encapsulation | | | Topology Management
| | | | | API/Tool
| v | | |
+---Executive Result Analysis | | +-- other APIs/Tools
| |
| |
| |
| |
+-----------------------v--+-----------------------------+
| Physical Network |
+--------------------------------------------------------+
Figure 3: Common processing flow of NMA
The common processing flow of NMA instance are shown in Figure 3.
The processing steps include:
1. User/Network Management Task Input: Input the user’s task
information Through multiple rounds of natural language
interaction.
2. Intent Analysis: Analysis user task intent through AI model
reasoning provided by the AI based basic services within NMA.
3. Task Decomposition: Split the task into detailed operations to be
performed based on the analyzed intent of the task.
4. Tool/API Invocation: Call the corresponding tool or function API
to complete the execution of each operation listed in step 3).
The toolkit refers to the collection of all tools that can be
used directly to manage and operate physical networks, which can
include management functions from existing MCS, EMS, or
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standalone other management tools. The toolkit can include
service configuration API/Tool, alarm monitor API/Tool,
performance monitor API/Tool, network optimization API/Tool,
topology management API/Tool, etc.
5. Process Encapsulation: Encapsulate each execution step.
According to the order or dependency of all the operations,
package the individual operation results into the execution
result of the entire task.
6. Executive result analysis: Analyze the task processing results
and return to the user.
Through above processing flow, NMA can achieve closed-loop automated
processing of tasks and constructing end-to-end intelligent network
maintenance assistance capabilities. For example, in the intelligent
troubleshooting scenario, NMA can identify the cause of the fault and
call the corresponding interfaces to handle it, such as creating a
troubleshooting order, automatically initiating rerouting/optical
power optimization, and other troubleshooting operations, and
automatically verifying the progress of the order execution, with
feedback on the troubleshooting results after the job order is
completed.
The introduction of NMA can effectively improve the level of
intelligent operation and maintenance of network, thus promoting the
continuous evolution of communication network towards higher-level
self-intelligence.
5. Related Interfaces
To be discussed in the later version.
6. Typical application scenarios after introducing NMA
Typical applications of NMA in networks can cover network operation
and maintenance and operation processes:
Network management and maintenance scenarios, including:
* Intelligent planning and construction: such as broadband
installation, resource/capacity planning, intelligent
acceptance, site selection, etc.
* Intelligent maintenance: such as intelligent fault diagnosis,
quality analysis, operation and maintenance/cutting assistant,
broadband maintenance assistant, etc.
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* Intelligent optimization: such as route optimization, coverage
optimization, topology optimization, and intelligent energy
saving, etc.
Network operation scenarios: including intelligent question and
answer, customer service assistant, automatic classification of
user complaints, customer retention, product recommendation,
automatic flow of work orders, anti-fraud monitoring and
identification, intelligent marketing and other value-added
services. This part is outside the scope of this document.
The starting point for the application of NMA in the live network
should comprehensively consider the scenarios with strong demand,
feasible technology, and good input-output ratio, and at the same
time meet the requirements of sufficient data for AI pre-training
during the construction of NMA instance, perfect data annotations,
and high fault tolerance rate. Based on above considerations, the
broadband installation and maintenance assistant, fault diagnosis,
operation and maintenance assistant may become the first application
scenarios.
7. Security Considerations
TBD.
8. IANA Considerations
This document has no requests for IANA action.
9. References
9.1. Normative References
9.2. 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-
03, 4 March 2024, <https://datatracker.ietf.org/doc/html/
draft-irtf-nmrg-ai-challenges-03>.
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[I-D.kdj-nmrg-ibn-usecases]
Yao, K., Chen, D., Jeong, J., Wu, Q., Yang, C., and L.
Contreras, "Use Cases and Practices for Intent-Based
Networking", Work in Progress, Internet-Draft, draft-kdj-
nmrg-ibn-usecases-01, 8 July 2024,
<https://datatracker.ietf.org/doc/html/draft-kdj-nmrg-ibn-
usecases-01>.
[RFC7575] Behringer, M., Pritikin, M., Bjarnason, S., Clemm, A.,
Carpenter, B., Jiang, S., and L. Ciavaglia, "Autonomic
Networking: Definitions and Design Goals", RFC 7575,
DOI 10.17487/RFC7575, June 2015,
<https://www.rfc-editor.org/rfc/rfc7575>.
[RFC7576] Jiang, S., Carpenter, B., and M. Behringer, "General Gap
Analysis for Autonomic Networking", RFC 7576,
DOI 10.17487/RFC7576, June 2015,
<https://www.rfc-editor.org/rfc/rfc7576>.
[RFC9222] Carpenter, B. E., Ciavaglia, L., Jiang, S., and P. Peloso,
"Guidelines for Autonomic Service Agents", RFC 9222,
DOI 10.17487/RFC9222, March 2022,
<https://www.rfc-editor.org/rfc/rfc9222>.
[RFC9315] Clemm, A., Ciavaglia, L., Granville, L. Z., and J.
Tantsura, "Intent-Based Networking - Concepts and
Definitions", RFC 9315, DOI 10.17487/RFC9315, October
2022, <https://www.rfc-editor.org/rfc/rfc9315>.
[TMF-IG1230]
Machwe, A., Milham, D., O’Sullivan, J., Clemm, A., and J.
Niemöller, "Autonomous Networks Technical Architecture",
TMF IG1230, December 2022.
Authors' Addresses
Xing Zhao
CAICT
Beijing
China
Email: zhaoxing@caict.ac.cn
Yunbin Xu
CAICT
Beijing
China
Email: xuyunbin@caict.ac.cn
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Chaode Yu
Huawei
China
Email: yuchaode@huawei.com
Haijun Meng
CAICT
Beijing
China
Email: menghaijun@caict.ac.cn
Yipeng Fu
CAICT
Beijing
China
Email: fuyipeng@caict.ac.cn
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