Considerations of network/system for AI services
draft-irtf-nmrg-ai-deploy-00
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| Document | Type | Active Internet-Draft (nmrg RG) | |
|---|---|---|---|
| Authors | Yong-Geun Hong , Joo-Sang Youn , Seung-Woo Hong , Ho-Sun Yoon , Pedro Martinez-Julia | ||
| Last updated | 2024-11-15 | ||
| RFC stream | Internet Research Task Force (IRTF) | ||
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draft-irtf-nmrg-ai-deploy-00
Internet Research Task Force Y-G. Hong
Internet-Draft Daejeon Univ.
Intended status: Informational J-S. Youn
Expires: 18 May 2025 DONG-EUI Univ.
S-W. Hong
H-S. Yoon
ETRI
P. Martinez-Julia
NICT
14 November 2024
Considerations of network/system for AI services
draft-irtf-nmrg-ai-deploy-00
Abstract
As the development of AI technology matured and AI technology began
to be applied in various fields, AI technology is changed from
running only on very high-performance servers with small hardware,
including microcontrollers, low-performance CPUs and AI chipsets. In
this document, we consider how to configure the network and the
system in terms of AI inference service to provide AI service in a
distributed method. Also, we describe the points to be considered in
the environment where a client connects to a cloud server and an edge
device and requests an AI service. Some use cases of deploying
network-based AI services, such as self-driving vehicles and network
digital twins, are described.
Status of This Memo
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This Internet-Draft will expire on 18 May 2025.
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Copyright Notice
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Provisions Relating to IETF Documents (https://trustee.ietf.org/
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Please review these documents carefully, as they describe your rights
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 3
2. Procedure to provide AI services . . . . . . . . . . . . . . 4
3. Network configuration structure to provide AI services . . . 6
3.1. AI inference service on Local machine . . . . . . . . . . 6
3.2. AI inference service on Cloud server . . . . . . . . . . 7
3.3. AI inference service on Edge device . . . . . . . . . . . 8
3.4. AI inference service on Cloud server and Edge device . . 9
3.5. AI inference service on horizontal multiple servers . . . 10
3.6. Network-side utilization for AI learning . . . . . . . . 11
4. Considerations of network/system for AI services . . . . . . 12
4.1. Considerations of the functional characteristics of the
hardware . . . . . . . . . . . . . . . . . . . . . . . . 12
4.2. Considerations for the characteristics of the AI model . 13
4.3. Considerations for the characteristics of the communication
method . . . . . . . . . . . . . . . . . . . . . . . . . 14
5. Addressing challenges for coupling AI and NM . . . . . . . . 15
5.1. Low-level challenges . . . . . . . . . . . . . . . . . . 15
5.2. High-level challenges . . . . . . . . . . . . . . . . . . 17
6. Use cases of deploying network-based AI services . . . . . . 17
6.1. Deploying AI services for self-driving vehicles . . . . . 18
6.2. Deploying AI services for network digital twins . . . . . 19
7. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 22
8. Security Considerations . . . . . . . . . . . . . . . . . . . 22
9. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . 22
10. References . . . . . . . . . . . . . . . . . . . . . . . . . 22
10.1. Normative References . . . . . . . . . . . . . . . . . . 22
10.2. Informative References . . . . . . . . . . . . . . . . . 22
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 24
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1. Introduction
In the Internet of Things (IoT), the amount of data generated from
IoT devices has exploded along with the number of IoT devices due to
industrial digitization and the development and dissemination of new
devices. Various methods are being tried to effectively process the
explosively increasing IoT devices and data of IoT devices. One of
them is to provide IoT services in a place located close to IoT
devices and users, away from cloud computing that transmits all data
generated from IoT devices to a cloud server [RFC9556].
IoT services also started to break away from the traditional method
of analyzing IoT data collected so far in the cloud and delivering
the analyzed results back to IoT objects or devices. In other words,
AIoT (Artificial Intelligence of Things) technology, a combination of
IoT technology and artificial intelligence (AI) technology, started
to be discussed at international standardization organizations such
as ITU-T. AIoT technology, discussed by the ITU-T CG-AIoT group, is
defined as a technology that combines AI technology and IoT
infrastructure to achieve more efficient IoT operations, improve
human-machine interaction, and improve data management and analysis
[CG-AIoT].
The first work started by the IETF to apply IoT technology to the
Internet was to research a lightweight protocol stack instead of the
existing TCP/IP protocol stack so that various types of IoT devices,
not traditional Internet terminals, could access the Internet
[RFC6574][RFC7452]. These technologies have been developed by
6LoWPAN working group, 6lo working group, 6tisch working group, core
working group, t2trg group, etc. As the development of AI technology
matured and AI technology began to be applied in various fields, just
as IoT technology was mounted on resource-constrained devices and
connected to the Internet, AI technology is also changed from running
only on very high-performance servers. The technology is being
developed to run on small hardware, including microcontrollers, low-
performance CPUs and AI chipsets. This technology development
direction is called On-device AI or TinyML[tinyML].
In this document, we consider how to configure the network and system
in terms of AI inference service to provide AI service in the IoT
environment. In the IoT environment, the technology of collecting
sensing data from various sensors and delivering it to the cloud has
already been studied by many standardization organizations including
the IETF and many standards have been developed. Now, after creating
an AI model to provide AI services based on the collected data, how
to configure this AI model as a system has become the main research
goal. Until now, it has been common to develop AI services that
collect data and perform inferences from the trained servers, but in
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terms of the spread of AI services, it is not appropriate to use
expensive servers to provide AI services. In addition, since the
server that collects and trains data mainly exists in the form of a
cloud server, there are also many problems in proceeding in the form
of requesting AI service by connecting a large number of terminals to
these cloud servers to provide AI services. Therefore, when an AI
service is requested to an edge device located at a close distance,
it may have effects such as real-time service support, network
traffic reduction, and important data security rather than requesting
an AI service to an AI server located in a distant cloud[RFC9556].
Even if an edge device is used to serve AI services, it is still
important to connect to an AI server in the cloud for tasks that take
a lot of time or require a lot of data. Therefore, an offloading
technique for properly distributing the workload between the cloud
server and the edge device is also a field that is being actively
studied. In this contribution, in the following proposed network
structure, the points to be considered in the environment where a
client connects to a server and an edge device and requests an AI
service are derived and described. That is, the following
considerations and options could be derived.
* AI inference service execution entity
* Hardware specifications of the machine to perform AI inference
services
* Selection of AI models to perform AI inference services
* A method of providing AI services from cloud servers or edge
devices
* Communication method to transmit data to request AI inference
service
The proposed considerations and items could be used to describe the
use case of self-driving vehicles and network digital twins. Since
providing AI services in a distributed method can provide various
advantages, it is desirable to apply it to self-driving vehicles and
network digital twins.
2. Procedure to provide AI services
Since research on AI services has been started for a long time, there
may be shapes to provide various types of AI services. However, due
to the nature of AI technology, in general, a system for providing AI
services consists of the following steps [AI_inference_archtecture]
[Google_cloud_iot].
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+-----------+ +-----------+ +-----------+ +-----------+ +-----------+
| Collect & | | Analysis &| | Train | | Deploy & | | Monitor & |
| Store |->| Preprocess|->| AI model |->| Inference |->| Maintain |
| data | | data | | | | AI model | | Accuracy |
+-----------+ +-----------+ +-----------+ +-----------+ +-----------+
|<--------->| |<------------------------>| |<--------->| |<--------->|
Sensor, DB AI Server Target AI Srvr &
machine Tgt. machine
|<---------------->|<--------------------->|<-------------->|<--------->|
Interent Local Internet Local &
Internet
Figure 1: AI service workflow
* Data collection & Store
* Data Analysis & Preprocess
* AI Model Training
* AI Model Deploy & Inference
* Monitor & Maintain Accuracy
In the data collection step, data required for training is prepared
by collecting data from sensors and IoT devices or by using data
stored in a database. Equipment involved in this step includes
sensors, IoT devices and servers that store them, and database
servers. Since the operations performed at this step are conducted
through the Internet, many IoT technologies studied by the IETF so
far have developed technologies suitable for this step.
In the data analysis and pre-processing step, the features of the
prepared data are analyzed and pre-processing for training is
performed. Equipment involved in this step includes a high-
performance server equipped with a GPU and a database server, and is
mainly performed in a local network.
In the model training step, a training model is created by applying
an algorithm suitable for the characteristics of the data and the
problem to be solved. Equipment involved in this step includes a
high-performance server equipped with a GPU, and is mainly performed
on a local network.
In the model deploying and inference service provision step, the
problem to be solved (e.g., classification, regression problem) is
solved using AI technology. Equipment involved in this step may
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include a target machine, a client, a cloud, etc. that provide AI
services, and since various equipment is involved in this stage, it
is conducted through the Internet. This document summarizes the
factors to be considered at this step.
In the accuracy monitoring step, if the performance deteriorates due
to new data, a new model is created through re-training, and the AI
service quality is maintained by using the newly created model. This
step is the same as described in the model training, model deploying,
and inference service provision steps described in the previous step
because re-training and model deploying are performed again.
3. Network configuration structure to provide AI services
In general, after training a AI model, the AI model can be built on a
local machine for AI model deploying and inference services to
provide AI services. Alternatively, we can place AI models on cloud
servers or edge devices and make AI service requests remotely. In
addition, for overall service performance, some AI service requests
to the cloud server and some AI service requests to edge devices can
be performed through appropriate load balancing.
3.1. AI inference service on Local machine
The following figure shows a case where a client module requesting AI
service on the same local machine requests AI service from an AI
server module on the same machine.
+---------------------------------------------------------------------+
| |
| +-----------------+ Request AI +-----------------+ |
| | Client module | Inference service | Server module | |
| | for AI service |----------------------->| for AI service | |
| | |<-----------------------| | |
| +-----------------+ Reply AI +-----------------+ |
| Inference result |
+---------------------------------------------------------------------+
Local machine
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Figure 2: AI inference service on Local machine
This method is often used when configuring a system focused on
training AI models to improve the inference accuracy and performance
of AI models without considering AI services or AI model deploying
and inference in particular. In this case, since the client module
that requests the AI inference service and the AI server module that
directly performs the AI inference service are on the same machine,
it is not necessary to consider the communication/network environment
or service provision method too much. Alternatively, this method can
be used when we want to simply decorate the AI inference service on
one machine without changing the AI service in the future, such as an
embedded machine or a customized machine.
In this case, a high level of hardware performance is not required to
train the AI model, but hardware performance sufficient to run the AI
inference service is required, so it is possible on a machine with a
certain amount of hardware performance.
3.2. AI inference service on Cloud server
The following figure shows the case where the client module that
requests AI service and the AI server module that directly performs
AI service run on different machines.
+--------------------------------------+
+------------------------+ | +---------------------------+ |
| +-----------------+ | | | +-----------------+ | |
| | Client module |<-+--------+-----+---->| Server module | | |
| | for AI service | | | | | for AI service | | |
| +-----------------+ | | | +-----------------+ | |
+------------------------+ | + --------------------------+ |
Local machine | Server machine |
+--------------------------------------+
Cloud (Internet)
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Figure 3: AI inference service on Cloud server
In this case, the client module requesting the AI inference service
runs on the local machine, and the AI server module that directly
performs the AI inference service runs on a separate server machine,
and this server machine is in the cloud network. In this case, the
performance of the local machine does not need to be high because the
local machine simply needs to request the AI inference service and,
if necessary, deliver only the data required for the AI service
request. For the AI server module that directly performs AI
inference service, we can set up our own AI server, or we can use
commercial clouds such as Amazon, Microsoft, and Google.
3.3. AI inference service on Edge device
The following figure shows the case where the client module that
requests AI service and the AI server module that directly performs
AI service are separated, and the AI server module is located in the
edge device.
+--------------------------------------+
+------------------------+ | +---------------------------+ |
| +-----------------+ | | | +-----------------+ | |
| | Client module |<-+--------+-----+---->| Server module | | |
| | for AI service | | | | | for AI service | | |
| +-----------------+ | | | +-----------------+ | |
+------------------------+ | + --------------------------+ |
Local machine | Edge device |
+--------------------------------------+
Edge network
Figure 4: AI inference service on Edge device
Even in this case, the client module that requests the AI inference
service runs on the local machine, the AI server module that directly
performs the AI inference service runs on the edge device, and the
edge device is in the edge network. Even in this case, the client
module that requests the AI inference service runs on the local
machine, the AI server module that directly performs the AI inference
service runs on the edge device, and the edge device is in the edge
network. The AI module that directly performs the AI inference
service on the edge device can directly configure the edge device or
use a commercial edge computing module.
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The difference from the above case where the AI server module is in
the cloud is that the edge device is usually close to the client,
whereas the performance is lower than that of the server in the
cloud, so there are advantages in data transfer time and inference
time, but in unit time Inference service performance is poor.
3.4. AI inference service on Cloud server and Edge device
The following figure shows the case where AI server modules that
directly perform AI services are distributed in the cloud and edge
devices.
+--------------------------------------+
+------------------------+ | +---------------------------+ |
| +-----------------+ | | | +-----------------+ | |
| | Client module |<-+---+----+-----+---->| Server module | | |
| | for AI service |<-+---+ | | | for AI service | | |
| +-----------------+ | | | | +-----------------+ | |
+------------------------+ | | + --------------------------+ |
Local machine | | Edge device |
| +--------------------------------------+
| Edge network
|
| +--------------------------------------+
| | +---------------------------+ |
| | | +-----------------+ | |
+----+-----+---->| Server module | | |
| | | for AI service | | |
| | +-----------------+ | |
| + --------------------------+ |
| Server machine |
+--------------------------------------+
Cloud (Internet)
Figure 5: AI inference service on Cloud sever and Edge device
There is a difference between the AI server module performed in the
cloud and the AI server module performed on the edge device in terms
of AI inference service performance. Therefore, the client
requesting the AI inference service may request by distributing the
AI inference service request to the cloud and edge device
appropriately in order to perform the desired AI service. In other
words, in the case of an AI service with low inference accuracy but
short inference time, we can request an AI inference service to the
edge device.
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3.5. AI inference service on horizontal multiple servers
In the previous section, to provide AI inference service, the network
configuration that consisted of local machines, edge devices, and
cloud servers is a kind of vertical hierarchy. Because the
capabilities of each machine are different, the overall performance
of the network using vertical hierarchy is dependent of each machine.
Generally, a cloud server has a most powerful performance and then an
edge device has the second powerful performance.
In this network configuration, AI service may have different
performance according to the load level of the server, computing
capability of the server machine and link-state between the local
machine and the server machines of the horizontal level. Thus, to
look for the server machine that can support the best AI service, it
is necessary for the network element that can monitor network link-
state and current state of the computing capability of the server
machines and the network load-balance that can perform a scheduling
policy of load balancing. The following figure shows the case where
the local machine that requests AI service to horizontal multiple
cloud servers.
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+--------------------------------------+
| +---------------------------+ |
| | +-----------------+ | |
+----+-----+---->| Server module | | |
| | | | for AI service | | |
| | | +-----------------+ | |
| | + --------------------------+ |
| | Server machine 1 |
| +--------------------------------------+
| Cloud (Internet)
|
| +--------------------------------------+
+------------------------+ | | +---------------------------+ |
| +-----------------+ | | | | +-----------------+ | |
| | Client module |<-+---+----+-----+---->| Server module | | |
| | for AI service |<-+---+ | | | for AI service | | |
| +-----------------+ | | | | +-----------------+ | |
+------------------------+ | | + --------------------------+ |
Local machine | | Server machine 2 |
| +--------------------------------------+
| Cloud (Internet)
|
| +--------------------------------------+
| | +---------------------------+ |
| | | +-----------------+ | |
+----+-----+---->| Server module | | |
| | | for AI service | | |
| | +-----------------+ | |
| + --------------------------+ |
| Server machine 3 |
+--------------------------------------+
Cloud (Internet)
Figure 6: AI inference service on horizontal multiple servers
3.6. Network-side utilization for AI learning
Collecting and preprocessing of data and training an AI model
requires a high-performance resource such as CPU, GPU, Power, and
Storage. To mitigate this requirement, we can utilize a network-side
configuration. Typically, federating learning is a machine learning
technique that trains an AI model across multiple decentralized
servers. It is a contrast to traditional centralized machine
learning techniques where all the local datasets are uploaded to one
server. In this federated learning, it enables multiple network
nodes to build a common machine learning model.
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And, transfer learning is a machine learning technique that focuses
on storing information gained while solving one problem and applying
it to a different but related problem. In this transfer learning, we
can utilize a network configuration to transfer common information
and knowledge between different network nodes.
4. Considerations of network/system for AI services
As described in the previous chapter, the AI server module that
directly performs AI inference services by utilizing AI models can be
performed on a local machine or a cloud server or an edge device.
In theory, if AI inference service is performed on a local machine,
AI service can be provided without communication delay time or packet
loss, but a certain amount of hardware performance is required to
perform AI service inference. So, in the future environment where AI
services become popular, such as when various AI services are
activated and AI services are disseminated, the cost of a machine
that performs AI services is important
If so, whether the AI inference service will be performed on the
cloud server or the discount price on the edge device can be a
determining factor in the system configuration.
4.1. Considerations of the functional characteristics of the hardware
When AI inference service request is made to a distant cloud server,
it may take a lot of time to transmit, but it has the advantage of
being able to perform many AI inference service requests in a short
time, and the accuracy of AI service inference increases.
Conversely, when an AI service request is made to a nearby edge
device, the transmission time is short, but many AI inference service
requests cannot be performed at once, and the accuracy of AI service
inference is lowered.
Therefore, by analyzing the characteristics and requirements of the
AI service to be performed, it is necessary to determine where to
perform the AI inference service on a local machine, a cloud server,
or an edge device.
The hardware characteristics of the machine performing the AI service
varies. In general, machines on cloud servers are viewed as machines
with higher performance than edge devices. However, the performance
of AI inference service varies depending on how the hardware such as
CPU, RAM, GPU, and network interface is configured for each cloud
server and edge device. If we do not think about cost, it is good to
configure a system for performing AI services with a machine with the
best hardware performance, but in reality, we should always consider
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the cost when configuring the system. So, according to the
characteristics and requirements of the AI service to be performed,
the performance of the local machine, cloud server, and edge device
must be determined.
Performance evaluation is possible through the performance matrix
presented in the standard of ETSI[MEC.IEG006]. The performance
metrics suggested by the ETSI standard are as follows. These metrics
is divided into two groups, namely Functional metrics, which assess
the user performance and include some classical indexes such as
latency in task execution, device energy efficiency, bit-rate, loss
rate, jitter, Quality of Service (QoS), etc.; and Non-functional
metrics that, instead, focus on the MEC(Mobile Edge Computing)
network deployment and management. Non-functional metrics include
the following indexes. Service life-cycle(instantiation, service
deployment, service provisioning, service update (e.g. service
scalability and elasticity), service disposal), service availability
and fault tolerance (aka reliability), service processing/
computational load, global mobile equipment host load, number of API
request (more generally number of events) processed/second on mobile
equipment host, delay to process API request (north and south),
number of failed API request. The sum of service instantiation,
service deployment, and service provisioning provide service boot-
time.
4.2. Considerations for the characteristics of the AI model
According to the characteristics of the AI service, although not
directly related to communication/network, the biggest influence on
AI inference services is the AI model to be used for AI inference
service. For example, in AI services such as image classification,
there are various types of AI models such as ResNet, EfficientNet,
VGG, and Inception. These AI models differ in AI inference accuracy,
but also in AI model file size and AI inference time. AI models with
the highest inference accuracy typically have very large file sizes
and take a lot of AI inference time. So, when constructing an AI
service system, it is not always good to choose an AI model with the
highest AI inference accuracy. Again, it is important to select an
AI model according to the characteristics and requirements of the AI
service to be performed.
Experimentally, it is recommended to use an AI model with high AI
inference accuracy in the cloud server, and use an AI model that can
provide fast AI inference service although the AI inference accuracy
is slightly lower for the fast AI inference service in the edge
device.
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It might be a bit of an implementation issue, but we should also
consider how we deliver AI services on cloud servers or edge devices.
With the current technology, a traditional web server method or a
server method specialized for AI service inference (e.g., Google's
Tensorflow Serving) can be used. Traditional web server methods such
as Flask and Django have the advantage of running on various types of
machines, but since they are designed to support general web
services, the service execution time is not fast. Tensorflow Serving
uses the features of Tensorflow to make AI service inference services
very fast and efficient. However, older CPUs that do not support AVX
cannot use the Tensorflow serving function because Google's
Tensorflow does not run. Therefore, rather than unconditionally
using the server method specialized in AI service inference, it is
necessary to decide the AI server module method that provides AI
services in consideration of the hardware characteristics of the AI
system that can be built.
4.3. Considerations for the characteristics of the communication method
The communication method for transferring data to request AI
inference service is also an important decision in constructing an AI
system. Using the traditional REST method, it can be used for
various machines and services, but its performance is inferior to
Google's gRPC. There are many advantages to using gRPC for AI
inference services because Google's gRPC enables large-capacity data
transfer and efficient data transfer compared to REST.
Cloud-edge collaboration-based AI service development is actively
underway. In particular, in the case of AI services that are
sensitive to network delays, such as object recognition and
autonomous vehicle services, (micro)services for inference are placed
on edge devices to obtain fast inference results and provide
services. As such, in the development of intelligent IoT services,
various devices that can provide computing services within the
network, such as edge devices, are being added as network elements,
and the number of IoT devices using them is rapidly increasing.
Therefore, a new function for computing resource management and
operation is required in terms of providing computing services within
the network. In addition, to operate distributed AI service on
network, the network policy for collaboration between edge devices
that support computing resource for AI service.
In network policy, in order to efficiently support distributed AI
services, existing networks must provide with the collaboration of AI
service between edge devices such as multi-edge network configuration
and AI service aware traffic steering in multi-edge network to
receive distributed AI service support efficiently in various network
environments that dynamically varies network resource and the
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computing resource of edge device. For example, in order to
efficiently provide distributed AI inference service in multi-edge
network environment, AI tasks message exchanges must be possible
between edge devices. Also, there are various delay sensitive AI
services based on edge device on network. They are divided into in-
time delay AI services with a deadline limit and on-time delay AI
services with the set of a time-range. In particular on-time delay
AI services want to return the results of prediction within the time
range. Therefore, distributed AI service must be able to is provided
proper AI service in terms of the delay service for distributed AI
service through network. Therefore, the client of AI service should
be able to be provided both in-time and on-time delay services and be
interacted with edge devices where distributed AI service is built to
provide both delay services.
5. Addressing challenges for coupling AI and NM
The document [I-D.irtf-nmrg-ai-challenges] describes the challenges
found in the application of AI to network management problems. They
are separated into low-level and high-level challenges. This section
describes how the concepts discussed in the present document are
linked to those challenges.
Some of the links between the structures presented in this document
and AI challenges involve the alignment with AINEMA
[I-D.pedro-nmrg-ai-framework].
AINEMA [I-D.pedro-nmrg-ai-framework] is a framework of functions
required for exploiting AI services and their interconnection.
Intelligent management systems based on AINEMA, as shown in
[TNSM-2018], introduce AI functions in the network management cycles,
and are able to express management decisions in terms of network
intents, as described in [I-D.pedro-ite].
5.1. Low-level challenges
The first challenge (C1) concerns to the combinatorial explosion of
the solution space in relation to the size of the problem. The
present document proposes to tackle part of such complexity by
separating the AI work in multiple elements of the network. The
separation is asymmetrical, so that some elements (cloud side) will
contribute more computation power to the distributed service.
The second challenge (C2) regards the dimensional and context
uncertainty and unpredictability. The present document focus on
cloistering the AI models and data within pre-defined client-edge-
cloud-server structures.
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The third challenge (C3) is to ensure that AI to is able to provide
prompt responses and decisions to management questions. The key
aspect discussed in this document to resolve this challenge is the
integration of end-node, in-network, edge, an cloud computing
services. This allows AI computations to be performed in the best
place possible. Time elapsed to get fast responses of easy
operations will be minimized by executing them in the end-nodes
(e.g., vehicles) and edge devices, while the time needed to compute
more complex operations is minimized by offloading them to cloud
computing services.
The fourth challenge (C4) states the difficulties related to
resolving data imperfections and scalability of techniques. This
challenge is specific to each information domain and problem.
However, as presented in this document, the mentioned difficulties in
C4 is relieved by exposing AINEMA [I-D.pedro-nmrg-ai-framework]
functions instantiated in cloud continuum to the AI services, so that
AI services transparently gain capabilities such as homogenization
and scalability. Instantiating a distributed AI system in a cloud
continuum enables the AI system to have many functions to deal with
data homogenization, resolving high data rates, etc.
The fifth challenge (C5) concerns the integration of AI services with
existing automation and human processes. The flexibility of the
structures presented in this document allow them to be connected to
existing systems, being aligned and somewhat interconnected with
AINEMA [I-D.pedro-nmrg-ai-framework]. The result is exemplified in
the support for network digital twin and vehicle environments.
Te sixth challenge (C6) exposes the need for cost-effective
solutions. Enabling AI services to be deployed in the cloud
continuum supports the maximization of effectiveness by dynamically
relocating services to the cheapest provider point that is able to
accomplish the computation requirements. This relocation is guided
by intents, as described in [I-D.pedro-ite], and implemented by an
independent management cycle, as described in AINEMA
[I-D.pedro-nmrg-ai-framework] and [TNSM-2018]. The result is that
the cost of a distributed AI system is continuously being checked and
adjusted by requesting the underlying infrastructure to modify the
point of instantiation of each part of the AI system.
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5.2. High-level challenges
The first challenge (H1) relies in the observation that AI techniques
were developed in a different area---imaging---, so they must be
extensively tailored for network problems. Although this is a quite
complex challenge, this document supports its resolution by enabling
AI models and algorithms to evolve separately. Multiple providers
will offer multiple solutions and they will be stitched together by
following the structures introduced above, as well as the functions
provided by the interconnection and alignment with AINEMA
[I-D.pedro-nmrg-ai-framework].
The second challenge (H2) conveys the mismatch that exists from the
original data and internal data used in AI models. Although the
present document does cover this mismatch, the structures presented
in this document help alleviate the burden by relocating some AI
processes as close as possible to the end-points (e.g., vehicles and
NDT). The same structures support the implementation of measures to
protect privacy. The AI services that are closer to the edges will
deal with sensitive data and ensure privacy protection by, for
example, anonymizing it. Those services are instantiated within the
data domain, so private data does not crosses the boundaries of its
data domain.
The third challenge (H3) consists on the level of acceptance that an
AI system experiences from administrators and operators. It is
agreed that giving full control of AI operations to administrators
and operators increases such level of acceptance. The structures
presented in this document support the involvement of administrators
and operators in AI system processes through the provision of
policies and network intents. On the one hand, aligning the
distributed AI system specified in this document with AINEMA
[I-D.pedro-nmrg-ai-framework], enables the enforcement high-level
policies and management goals provided by administrators and
operators. On the other hand, aligning the distributed AI system
specified in this document with intent-based networking systems,
particularly intent translation [I-D.pedro-ite], enables
administrators and operators to use high-level specifications (namely
network intents) to communicate the policies, requirements, and goals
that must be enforced to the AI system constructed over the cloud
continuum.
6. Use cases of deploying network-based AI services
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6.1. Deploying AI services for self-driving vehicles
Various sensors are used in self-driving vehicles, and the final
judgment is made by combining these data. Among them, camera data-
based object detection solves parts that expensive equipment such as
LiDAR and RADAR cannot solve. Camera-based object detection performs
various tasks, and in addition to lane recognition for maintaining
driving lanes and changing lanes, it also supports safe driving and
parking assistance by distinguishing shape information such as
pedestrians, signs, and parking vehicles along the road.
In order to perform such driving assistance and autonomous driving,
object detection needs to be performed in real time. The minimum
FPS(Frames Per Second) to be considered real-time in autonomous
driving is 30 FPS[Object_detection]. No matter how high the accuracy
is, it cannot be used for autonomous driving if it does not meet the
corresponding reference value.
Task offloading refers to a technology or structure that transfers
computing tasks to other processing devices or systems to perform
them. Task offloading can quickly process tasks that exceed the
performance limits of devices that lack resources by delivering tasks
from devices with limited computing power, storage space, and power
to devices that are rich in computing resources.
For devices with low hardware performance (e.g., NVIDIA Jetson Nano
board, Qual-core ARM A57, 4GB RAM), all locally without task
offloading results in 4.6 FPS, which is difficult to perform object
detection-based autonomous driving. On the other hand, if task
offloading is applied to perform object detection on devices with
high hardware performance (e.g., Intel i7, RTX 3060, 32GB RAM) and
the rest of the work is performed on the client, 41.8 FPS will be
obtained. This is a result that satisfies 30 FPS, which is the
reference FPS of object detection-based autonomous driving.
In the case of AI services such as object detection, if it is
difficult to perform on resource-constrained devices, it can be seen
that the task offloading structure shows some efficiency. However,
without performing all operations locally, task offloading operations
between network nodes can affect the entire time because the larger
the size of the data, the greater the communication latency.
Therefore, in such a network distributed environment, the provision
of AI services should be designed in consideration of various
variables. The Figure 7 shows an example of distributed AI
deployment in a self-driving vehicle when it does not have enough
capabilities to proceed the object detection operation in real-time
and it asks some tasks to edge devices and cloud servers.
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+--------------------------------------+
+------------------------+ | +---------------------------+ |
| +-----------------+ | | | +-----------------+ | |
| |Object detection |<-+---+----+-----+---->|Object detection | | |
| | service |<-+---+ | | | service | | |
| +-----------------+ | | | | +-----------------+ | |
+------------------------+ | | + --------------------------+ |
Vehicle | | Edge device |
| +--------------------------------------+
| Edge network
|
| +--------------------------------------+
| | +---------------------------+ |
| | | +-----------------+ | |
+----+-----+---->|Object detection | | |
| | | service | | |
| | +-----------------+ | |
| + --------------------------+ |
| Server machine |
+--------------------------------------+
Cloud (Internet)
Figure 7: Distributed object detection service in self-driving
vehicle
6.2. Deploying AI services for network digital twins
Network digital twin also need to build distributed AI services. The
purpose of a network digital twin is described in
[I-D.irtf-nmrg-network-digital-twin-arch]. In particular, the
network digital twin provides network operators with technology that
enables stable operation of the physical network and stable execution
of optimal network policies and deployment procedures. To achieve
this, the network digital twin will use AI capabilities for various
purposes.
Various AI functions will be applied for optimal network operation
and management. However, the actual physical network consists of
many network devices and has a complex structure. In addition, in a
large-scale network environment, the network overhead is very large
to collect and store information from many network devices in a
centralized manner, and to create and operate network operation
policies based on it.
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Therefore, there is a need for a method to apply AI functions based
on a distributed form for network operation and management. In
particular, the actual physical network structure is built in a
logical hierarchical structure. Therefore, it is necessary to apply
a distributed AI method that considers the logical hierarchical
network structure environment.
In order to optimally perform network operation and management
through distributed AI methods, it is necessary to generate AI
function-based network operation and management policy models and an
operational method to distribute the generated AI function-based
network policies. In particular, in order to operate a network
digital twin in a large-scale network environment, it is necessary to
generate AI-based network policy models in a distributed manner. A
federated learning algorithm or a transfer learning algorithm that
can learn large-scale networks in a distributed manner can be
applied.
+-----------------------------------------------------+
| |
| Distributed network learning model |
| in large-scale network environment |
| |
| +-------------+------------+ |
| | Master | |
| | (AI based Policy model) | |
| +-------------+------------+ |
| | |
| +-----------+-----+-----+-----------+ |
| | | | | |
| +----+----+ +----+----+ +----+----+ +----+----+ |
| | Worker | | Worker | | Worker | | Worker | |
| | (Agent) | | (Agent) | | (Agent) | | (Agent) | |
| +----+----+ +----+----+ +----+----+ +----+----+ |
| | | | | |
| +----+----+ +----+----+ +----+----+ +----+----+ |
| | Local | | Local | | Local | | Local | |
| | Data | | Data | | Data | | Data | |
| | Repo- | | Repo- | | Repo- | | Repo- | |
| | sitory | | sitory | | sitory | | sitory | |
| +---------+ +---------+ +---------+ +---------+ |
| |
+-----------------------------------------------------+
Figure 8: Distributed learning model of network learning for
network digital twins
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As shown in Figure 8, in order to learn a large-scale network through
a distributed learning method, a local data repository to store
network data must be established in each region, for example, based
on location or AS (Autonomous System). Therefore, the distributed
learning method learns through each worker (agent) based on the local
network data stored in the local network data repository, and
generates a large-scale network policy model through the master.
This distributed learning method can reduce the network overhead of
centralized data collection and storage, and reduce the time required
to create AI models for network operation and management policies for
large-scale networks. In addition, the network policy model
generated by the worker can be used as a locally optimized network
policy model to provide AI-based network operation and management
policy services optimized for local network operations.
The distributed deployment of trained AI network policy models can be
deployed on network devices that can manage and operate the local
network to minimize network data movement. For example, in a large-
scale network consisting of multiple ASes, AI network policy models
can be deployed per AS to optimize network operation and management.
Figure 9 shows an example of operating and managing a network by
distributing AI network policy models by AS.
+---------------------------------------------------------+
| +-------------+------------+ |
| | Master | |
| | (AI-based Network Policy | |
| | model management) | |
| +-------------+------------+ |
| | |
| +-------------+-------------+ |
| Worker | Worker | |
| +----------+----------+ +----------+----------+ |
| | Network device | | Network device | |
| | (AI-based network | | (AI-based network | |
| | Policy model) | | Policy model) | |
| +----------+----------+ +----------+----------+ |
| | | |
| +----------+----------+ +----------+----------+ |
| | AS_1 | | AS_2 | |
| +---------------------+ +---------------------+ |
+---------------------------------------------------------+
Figure 9: Distributed deployment of trained AI network policy models
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7. IANA Considerations
There are no IANA considerations related to this document.
8. Security Considerations
When AI service is performed on a local machine, there is no security
issue, but when AI service is provided through a cloud server or edge
device, IP address and port number may be known to the outside can
attack. Therefore, when providing AI services by utilizing machines
on the network such as cloud servers and edge devices, it is
necessary to analyze the characteristics of the modules to be used
well, identify vulnerabilities in security, and take countermeasures.
9. Acknowledgements
TBA
10. References
10.1. Normative References
[RFC6574] Tschofenig, H. and J. Arkko, "Report from the Smart Object
Workshop", RFC 6574, DOI 10.17487/RFC6574, April 2012,
<https://www.rfc-editor.org/info/rfc6574>.
[RFC7452] Tschofenig, H., Arkko, J., Thaler, D., and D. McPherson,
"Architectural Considerations in Smart Object Networking",
RFC 7452, DOI 10.17487/RFC7452, March 2015,
<https://www.rfc-editor.org/info/rfc7452>.
[RFC9556] Hong, J., Hong, Y., de Foy, X., Kovatsch, M., Schooler,
E., and D. Kutscher, "Internet of Things (IoT) Edge
Challenges and Functions", RFC 9556, DOI 10.17487/RFC9556,
April 2024, <https://www.rfc-editor.org/info/rfc9556>.
10.2. Informative References
[I-D.irtf-nmrg-network-digital-twin-arch]
Zhou, C., Yang, H., Duan, X., Lopez, D., Pastor, A., Wu,
Q., Boucadair, M., and C. Jacquenet, "Network Digital
Twin: Concepts and Reference Architecture", Work in
Progress, Internet-Draft, draft-irtf-nmrg-network-digital-
twin-arch-08, 16 October 2024,
<https://datatracker.ietf.org/doc/html/draft-irtf-nmrg-
network-digital-twin-arch-08>.
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[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>.
[I-D.pedro-nmrg-ai-framework]
Martinez-Julia, P., Homma, S., and D. Lopez, "Artificial
Intelligence Framework for Network Management", Work in
Progress, Internet-Draft, draft-pedro-nmrg-ai-framework-
04, 21 October 2023,
<https://datatracker.ietf.org/doc/html/draft-pedro-nmrg-
ai-framework-04>.
[I-D.pedro-ite]
Martinez-Julia, P. and J. P. Jeong, "Intent Translation
Engine for Intent-Based Networking", Work in Progress,
Internet-Draft, draft-pedro-ite-01, 4 March 2024,
<https://datatracker.ietf.org/doc/html/draft-pedro-ite-
01>.
[CG-AIoT] "ITU-T CG-AIoT", <https://www.itu.int/en/ITU-T/
studygroups/2017-2020/20/Pages/ifa-structure.aspx>.
[tinyML] "tinyML Foundation", <https://www.tinyml.org/>.
[AI_inference_archtecture]
"IBM Systems, AI Infrastructure Reference Architecture",
<https://www.ibm.com/downloads/cas/W1JQBNJV>.
[Google_cloud_iot]
"Bringing intelligence to the edge with Cloud IoT",
<https://cloud.google.com/blog/products/gcp/bringing-
intelligence-edge-cloud-iot>.
[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.
[Object_detection]
Lewis, "Object Detection for Autonomous Vehicles Gene",
2016.
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[TNSM-2018]
P. Martinez-Julia, V. P. Kafle, and H. Harai, "Exploiting
External Events for Resource Adaptation in Virtual
Computer and Network Systems, in IEEE Transactions on
Network and Service Management. Vol. 15, n. 2, pp. 555--
566, 2018.", 2018.
Authors' Addresses
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
Seung-Woo Hong
ETRI
218 Gajeong-ro Yuseong-gu
Daejeon
34129
South Korea
Phone: +82 42 860 1041
Email: swhong@etri.re.kr
Ho-Sun Yoon
ETRI
218 Gajeong-ro Yuseong-gu
Daejeon
34129
South Korea
Phone: +82 42 860 5329
Email: yhs@etri.re.kr
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Pedro Martinez-Julia
National Institute of Information and Communications Technology
4-2-1, Nukui-Kitamachi, Koganei, Tokyo
184-8795
Japan
Phone: +81 42 327 7293
Email: pedro@nict.go.jp
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