Network Management Intent -One of
IBN Use CasesChina MobileBeijing100053Chinachendanyang@chinamobile.comChina MobileBeijing100053Chinayaokehan@chinamobile.comXidian UniversityXi'an710071Chinacgyang@xidian.edu.cnXidian UniversityXi'an710071Chinaxinrum@163.comXidian UniversityXi'an710071Chinayingouyang224@163.com
Networking
Internet Research Task ForceIntent based network, network managementFull life cycle network management will be a key feature of the
future communication networks. Meanwhile, the complexity of the network
management should be reduced and the network expects to be managed in a
fully automated manner with humans out of the loop. In this document, we
propose an use case of intent based network management to achieve more
flexible , convenient, and efficient network management. In this use
case, we propose an architecture and attempt to illustrate the five
levels of achieving full autonomous network management.The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
"SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
document are to be interpreted as described in RFC 2119.With the rapid evolution of networks, such as the emergence of the
sixth generation (6G), we have entered a new digital era that is
ubiquitously connected by highly heterogeneous and dynamic network
infrastructure. And various network applications are predicted to appear
more in future communication networks. With these complex network
scenarios, services, and uncountable degrees of freedom in future
communication networks, the complexity of network management will
increase drastically. Traditional network management methods are
insufficient to keep up with the growing requirements. Firstly, there
exists high complexity and difficulty to manage a large amount of
infrastructures due to high labor cost, high error probability, low
management efficiency. Secondly, traditional network management lacks
closed-loop control, which can not find and repair faults in time. To
overcome these challenges, some novel network models have been proposed,
such as NFV and SDN. However, it still faces many novel challenges:Tight coupling of network and service applications: Traditional
network management methods cannot solve the diverse cross-domain
service requirements in future communication networksHigh configuration complexity and scaling cost: Future
communication networks will have a huge network scale, different
kinds of network resources, and constantly changing topology. This
will result in a high network configuration complexity and cannot be
configured in a timely and efficient manner. A smart and simple
network management architecture is required for rapid deployment of
network service requirementsFragile performance provision and policy robustness: In future
communication networks, it need to continuously improve the network
system model based on the experienced knowledge of administrators.
And real-time verification and feedback mechanisms are required for
network runtime.Lack of full life cycle verification of policy resilience: Manual
management has several shortcomings, such as high error probability,
long fault location time, and expensive labor costs. Fortunately,
full life cycle verification can reduce the failure recovery time,
monitor network operation, and maintain the whole process while
enhancing the accuracy of policy configuration and the robustness of
policy implementation.At the same time, with the exponential growth of network devices,
network administrators need to put in tremendous effort to manage the
policies that affect the services of these devices. While in recent
years the network management methods have been gradually automated,
there are still many procedures that must be accomplished under the
strict supervision of administrators, resulting in high error
probability. Moreover, current network management is at a level too low
to entirely eliminate the requirements to customize each solution for a
specific device or protocol in use. The emergence of the IBN, as defined
in RFC 9315, has the potential to
compensate for the limitations of the current network management
methods. The intent is a high-level abstraction of policy, and the IBN
is a way to manage the network through intent-driven rather than a
low-level configuration. When the user specifies a high-level goal
(intent), the network automatically converts that goal into policies and
automatically deploys these policies throughout the network.IBN: Intent based networkIBNM: Intent based network managementDQN: Deep Q networkDriven by the above requirements and challenges, the intent based
network management (IBNM) is proposed. IBNM aims to transition from a
fully static manual network management to a fully dynamic autonomous
intent based network management. In IBNM, users express their service
requirements or goals in a declarative manner without paying attention
to how the network achieves them.The architecture is shown as Fig 1, which includes the application
layer, the intent-enabled layer and the infrastructure layer. The
application layer collects intents from various users and applications,
and provides a number of programmable network management services. The
intent-enabled layer consists of the intent translation module,
intelligent policy mapping module, and intent guarantee module, whose
functions are to build a bridge between the application layer and the
infrastructure layer. Heterogeneous physical devices are deployed in the
infrastructure layer. This layer can execute management instructions
from the intent-enabled layer and upload underlying network situation
information to the intent-enabled layer. Information interaction between
different layers is done through different interfaces, such as the
northbound and southbound interfaces.Among these layers, the intent-enabled layer is the core of this
network management architecture. First, the intent translation module
translates declarative intent (expressed in the form of natural
language) into the network intent that can be recognized by the system
(specific ). There may be an intent conflict problem when multiple
intents are input simultaneously. Thus, the intent translation module
executes intent verification and intent conflict resolution before the
intent is issued (measurable). Second, the intelligent policy mapping
module provides customized policies (achievable) for specific intents
according to various requirements and evaluates the current policy by
rewarding values. After that, in order to complete the policy
configuration within the time-bound, the intent guarantee module is
needed to execute feature extraction and location on the collected alarm
information. Then the fault information is fed back to the intelligent
policy mapping module.Based on the above design, on one hand, this architecture can achieve
full lifecycle automated network management with humans out of the loop.
On the other hand, it converts service requirements (intent) into
network policies and provides self-adapted customized service with a
full lifecycle verification. The functions of each module in
intent-enabled layer are described below.Intent Translation ModuleIntent is expressed in the form of natural language, which is
ambiguous and does not follow specific forms. Thus, the intent
translation module translates the intent expressed in a natural
language through bidirectional long short-term memory (Bi-LSTM) and
morphological rules, and outputs the network’s understandable
and regularized intent. Meanwhile, the intent translation module
analyzes the accuracy and completeness of the translated intent
through intent verification and conflict resolution, and
continuously monitors whether there are conflicts. The intent
translation module is the first step to realizing intent. In short,
the translation module provides a bridge between the users and the
network and is responsible for ensuring the integrity and
realizability of the input intent. In addition, the result of the
intent translation module is a critical foundation for the
intelligent policy mapping module.Intelligent Policy Mapping ModuleThe intelligent policy mapping module is the process of intent
realization, in can consist of policy repository, fuzzy decision
tree, and deep Q network (DQN). A large number of atomic policies
can be stored in the policy repository, which can be established by
the historical policy and administrator operation and maintenance
experience. In particular, atomic policy refers to the smallest
policy unit that can be executed but cannot be split again, such as
some functional node configuration policies (routing selection,
service provider node selection, etc.). The function of the fuzzy
decision tree is to generate new atomic policies for the policy
repository or to adjust the existing atomic policies. DQN is a
combination of neural network and reinforcement learning, which are
used to reorganize the atomic policies into a new policy that
satisfies the current intent requirements. The function of the
neural network is to calculate the configuration scores for
state-action pairs and outputs all actions. Then the action with the
highest configuration score is selected as the configuration action
according to the Q-learning principle.Intent Guarantee ModuleThe function of the intent guarantee module is to monitor the
network in real time, collect and analyze the data of the faults
that have occurred, and predict the faults that have not occurred in
order to ensure the normal operation of the network. First, a fault
information table, including fault type, location, quantity, and
occurrence time, is established based on the collected network
abnormal information. Second, a deep neural evolutionary network is
used to repair faults and feed back the repair results to the intent
translation module in real time. A deep neural evolution network can
not only repair faults, but also ensure the implementation of
intents when network faults occur.The advantages of the intent based network management include:A specific intent based network management architecture is
proposed for sensing diversified service intents with various
scenarios, objectives, and preferences.The proposed framework can achieve zero touch management within a
time bound, which can handle the complex services in a fully
automatic manner.The performance benefits are in terms of adaptivity and
scalability, which rewards various network environments and avoids
network reconstructions.TBDTBDTBDThis document has no requests to IANA.