DETNET J. Zhao
Internet-Draft CAICT
Intended status: Standards Track Q. Xiong
Expires: 4 September 2025 ZTE Corporation
Z. Du
China Mobile
M. Jadoon
L.M. Contreras
InterDigital
3 March 2025
Enhanced Use Cases for Scaling Deterministic Networks
draft-zhao-detnet-enhanced-use-cases-03
Abstract
This document describes use cases and network requirements for
scaling deterministic networks which is not covered in RFC8578, such
as industrial internet, high experience video, intelligent computing,
and ISAC-enabled smart factory and outlines the common properties
implied by these use cases.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 3
1.1. Requirements Language . . . . . . . . . . . . . . . . . . 3
2. Terminology . . . . . . . . . . . . . . . . . . . . . . . . . 3
3. Enhanced Use Cases and Network Requirements . . . . . . . . . 4
3.1. Industrial Internet . . . . . . . . . . . . . . . . . . . 4
3.1.1. Use Case Description . . . . . . . . . . . . . . . . 4
3.1.1.1. Machine Vision . . . . . . . . . . . . . . . . . 4
3.1.1.2. Remote Control . . . . . . . . . . . . . . . . . 5
3.1.1.3. AGV Intelligent Control . . . . . . . . . . . . . 6
3.1.1.4. AR Assistance . . . . . . . . . . . . . . . . . . 6
3.1.2. Requests to the IETF . . . . . . . . . . . . . . . . 7
3.2. High Experience Video . . . . . . . . . . . . . . . . . . 8
3.2.1. Use Case Description . . . . . . . . . . . . . . . . 8
3.2.1.1. Cloud VR and AR . . . . . . . . . . . . . . . . . 8
3.2.1.2. Cloud Games . . . . . . . . . . . . . . . . . . . 9
3.2.1.3. Cloud Live Streaming . . . . . . . . . . . . . . 10
3.2.2. Requests to the IETF . . . . . . . . . . . . . . . . 10
3.3. Intelligent Computing . . . . . . . . . . . . . . . . . . 10
3.3.1. Use Case Description . . . . . . . . . . . . . . . . 11
3.3.1.1. Scientific Research . . . . . . . . . . . . . . . 11
3.3.1.2. Autonomous Vehicles . . . . . . . . . . . . . . . 11
3.3.2. Requests to the IETF . . . . . . . . . . . . . . . . 12
3.4. ISAC-Enabled Smart Factory . . . . . . . . . . . . . . . 12
3.4.1. Use Case Description . . . . . . . . . . . . . . . . 13
3.4.1.1. Predictive Maintenance . . . . . . . . . . . . . 14
3.4.1.2. Real-Time Process Optimization . . . . . . . . . 14
3.4.1.3. Safety Control and Maintenance . . . . . . . . . 15
3.4.2. Requests to the IETF . . . . . . . . . . . . . . . . 16
3.5. Interconnection of Time Sensitive Domains . . . . . . . . 16
3.5.1. Use Case Description . . . . . . . . . . . . . . . . 16
3.5.2. Requests to the IETF . . . . . . . . . . . . . . . . 17
4. Use Case Common Themes . . . . . . . . . . . . . . . . . . . 17
4.1. Requirements with Deterministic Classification . . . . . 17
4.2. Requirements with Ultra-low or Zero Packet Loss . . . . . 18
5. Security Considerations . . . . . . . . . . . . . . . . . . . 19
6. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 19
7. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . 19
8. References . . . . . . . . . . . . . . . . . . . . . . . . . 19
8.1. Informative References . . . . . . . . . . . . . . . . . 19
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 20
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1. Introduction
According to [RFC8655], Deterministic Networking (DetNet) operates at
the IP layer and delivers service which provides extremely low data
loss rates and bounded latency within a network domain. The bounded
latency indicates the minimum and maximum end-to-end latency from
source to destination and bounded jitter (packet delay variation).
[RFC8578] has presented use cases for diverse industries and these
use cases differ in their network topologies and requirements. It
should provide specific desired behaviors in DetNet.
[I-D.ietf-detnet-scaling-requirements] focus on the scaling
deterministic networks and describes the enhanced requirements for
DetNet enhanced data plane including the deterministic latency
guarantees and it also mentioned the enhanced DetNet should support
different levels of application requirements which is important for
the DetNet deployment. There are a variety of use cases in scaling
deterministic networks which is not covered in [RFC8578]. It is
required to provide the typical use cases for scaling deterministic
networks and analyze the SLAs requirements and desired behaviors in
enhanced DetNet.
The industries covered by the use cases in this document are:
* Industrial Internet (section 3.1)
* High Experience Video (section 3.2)
* Intelligent Computing (section 3.3)
* ISAC-Enabled Smart Factory(section 3.4)
This document describes use cases and network requirements for
scaling deterministic networks including industrial internet, high
experience video and intelligent computing and outlines the common
properties implied by these use cases.
1.1. Requirements Language
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 [RFC2119].
2. Terminology
The terminology is defined as [RFC8655] and [RFC8578].
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3. Enhanced Use Cases and Network Requirements
3.1. Industrial Internet
3.1.1. Use Case Description
In the industrial internet, the entire industrial process can be
roughly divided into research and development design, production
manufacturing, operation and maintenance services. The typical
application prospects of deterministic networks mainly include ultra-
high definition video, cloud-based robots, remote control, machine
vision, and cloud-based AGV. The scenarios such as machine vision,
AGV intelligent control, remote control, and AR assisted robotic arm
control demand deterministic requirements.
3.1.1.1. Machine Vision
The machine vision system needs to achieve real-time remote
monitoring function, which requires high-speed and large connectivity
characteristics. It can monitor the production process execution
management system (MES) of manufacturing enterprises through mobile
and portable terminals without entering the workshop, and obtain the
operating status of the visual inspection system, such as normal
operating time, effective operating time, fault cause etc. It is
bandwidth sensitive and demand cloud-based deployment and wide area
networks requirements.
The following table shows the main network requirements of machine
vision.(These metrics are based on 3GPP Standard 3GPP TS 22.104, 3GPP
TR 22.261, and 3GPP TR 22.829.)
+---------------------------------+---------------------------------+
| Machine Vision Requirement | Attribute |
+---------------------------------+---------------------------------+
| Bandwidth | Real time upload of image |
| | information:>50M |
| | |
| One-way maximum delay | 10 ms |
| | |
| Availability | 99.99% |
+---------------------------------+---------------------------------+
Figure 1: Requirements of Machine Vision
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3.1.1.2. Remote Control
Remote control can ensure personnel safety, improve production
efficiency, and achieve assistance from multiple production units.
In order to achieve the effect of remote control, the controller
needs to send status information to the controller through a
communication network based on remote perception. The controller
analyzes and makes decisions based on the received status
information, and then sends corresponding action instructions to the
controller through the communication network. The controller
executes the corresponding actions based on the received action
instructions, completing the remote control process. In order to
guarantee control effectiveness, communication network latency,
jitter, and reliability are even more important. The typical
application is cloud-based PLC (Programmable Logic Controller). It
is jitter sensitive and cloud-based PLC demand wide area networks
requirements.
The following table describes requirements of Cloud-based PLC.
(These metrics are based on 3GPP Standard 3GPP TS 22.104, 3GPP TR
22.261, and 3GPP TR 22.829.)
+-------------------------------+-----------------------------------+
| Cloud-based PLC Requirement | Attribute |
+-------------------------------+-----------------------------------+
| Bandwidth | Image/video stream upload, |
| | upstream>50Mbps; |
| | PLC control command issued, |
| | downstream>50kbps; |
| | |
| One-way maximum delay |Within workshop level equipment:1ms|
| |Workshop level equipment room:10ms |
| |Remote operation in the park/city/ |
| |wide area: image upstream:20ms; |
| |Command issuance:10ms; |
| | |
| Maximum jitter | Less than 100 us |
| | |
| Availability | 99.999% |
+-------------------------------+-----------------------------------+
Figure 2: Requirements of Cloud-based PLC
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3.1.1.3. AGV Intelligent Control
Automated Guided Vehicle (AGV) is an intelligent device widely used
in highly automated places such as factory workshops, airports,
ports, freight warehouses, etc. It generally consists of three
parts: walking, navigation, and control systems. The automated AGV
is equipped with a camera to capture the scene in front of the
vehicle and upload it to the MEC and navigation system in real-time
through a 5G module for image analysis and route planning, achieving
fully automated logistics transportation. AGV has a certain driving
speed and is often used in cluster operation scenarios. Therefore, a
network connection with high deterministic delay and jitter is
required to transmit control signals.
The following table describes requirements of AGV intelligent
control.(These metrics are based on 3GPP Standard 3GPP TS 22.104,
3GPP TR 22.261, and 3GPP TR 22.829.)
+-----------------------------+--------------------------------------+
| AGV Intelligent Control | |
| Requirement | Attribute |
+-----------------------------+--------------------------------------+
| Bandwidth |Schedule communication:>1Mbps, |
| |Real time communication:1Mbps~200Mbps |
| |Visual: 10Mbps~1Gbps |
| | |
| One-way maximum delay |Schedule communication:100ms |
| |Dispatching communication:100ms |
| |Real time communication:20ms~40ms |
| |Visual: 10ms~100ms |
| Availability | 99.9999% |
+-----------------------------+--------------------------------------+
Figure 3: Requirements of AGV Intelligent Control
3.1.1.4. AR Assistance
With the intelligent and networked transformation and upgrading of
industrial manufacturing equipment, more and more AR assisted
intelligent robots will be used in advanced manufacturing. At the
same time, there are scenarios where multiple robot systems work
together, such as welding, stamping, etc. The robotic arm is the
most widely used automated mechanical device in the field of robotics
technology, in areas such as industrial manufacturing, medical
treatment, entertainment services, military, semiconductor
manufacturing, and space exploration. The more axis joints of the AR
assisted robotic arm, the higher the degree of freedom, and the
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larger the angle of the operating range.
The following table describes requirements of AR Assistance. (These
metrics are based on 3GPP Standard 3GPP TS 22.104, 3GPP TR 22.261,
and 3GPP TR 22.829.)
+---------------------------+----------------------------+
| AR Assistance Requirement| Attribute |
+---------------------------+----------------------------+
| Bandwidth | Maintenance guidance: |
| | downstream>50Mbps |
| | upstream > 20Mbps |
| | downstream>50kbps |
| | Auxiliary assembly: >50Mbps|
| | downstream: 1Mbps~30Mbps |
| | |
| One-way maximum delay |Maintenance guidance:20ms |
| |Auxiliary assembly:10ms |
| | |
| Maximum jitter | Less than 500 us |
| | |
| Availability | 99.999% |
+---------------------------+----------------------------+
Figure 4: Requirements of AR Assistance
3.1.2. Requests to the IETF
* Real-time remote monitoring, which requires high-speed
connectivity
* Cloud-based deployment, which requires transmission through
heterogeneous networks
* Cloud-based centralized management
* Remote control is jitter sensitive, e.g. less than 100us
* Industrial camera images with high definition, with little or no
compression, which requires high bandwidth
* Low end-to-end delay requirements differ from applications and
services, such as 10ms and 20ms
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3.2. High Experience Video
3.2.1. Use Case Description
High Experience Video refers to video content that delivers an
exceptional viewing experience through advanced technologies and
production techniques. It demands high-quality transmission to
ensure that the content is delivered without compromising its
integrity and impact. High Experience Video relies on deterministic
networks to deliver the best possible viewing experience, which
requires a combination of low latency, low jitter, high bandwidth,
and high reliability. The typical scenarios of High Experience Video
involve applications that have high requirements for video quality,
transmission speed, and user experience such as cloud VR and AR,
cloud games and cloud live streaming.
3.2.1.1. Cloud VR and AR
Augmented Reality (AR) or Virtual Reality (VR) media applications,
collectively called eXtended Reality (XR) applications place
extremely high demands on network transmission including high
throughput, low latency, and high reliability. The key feature of
cloud VR/AR is that content and rendering is on the cloud. By
utilizing the cloud capabilities, VR/AR user experience is improved
and terminal costs are reduced. Cloud AR/VR services are latency
sensitivity, and different levels of experience require
differentiated latency. Cloud VR/AR rendering and streaming latency
are divided into three parts: cloud processing, network transmission,
and terminal processing. Cloud VR/AR operation latency is divided
into cloud rendering latency and terminal secondary rendering and
refresh rendering processes.
Moreover, AR/VR applications typically involve a large amount of data
transmission, such as high-definition video streams, real-time
rendering data. For some cases, a single packet loss during
transmission will it affect the integrity of the entire application.
So AR/VR applications require ultra-low packet loss such as no more
then 0.001% and for particular packets, it demands zero packet loss.
The following table describes requirements of Cloud VR/AR. (These
metrics are based on 3GPP TR 22.261).
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+----------------------+-----------+---------------------+----------------+
| Requirement | Bandwidth |One-way maximum delay|Packet loss rate|
+----------------------+-----------+---------------------+----------------+
| Cloud VR/AR Video |downstream | 50ms |no more than |
| comfortable | >75Mbps | |0.001% |
| experience | | | |
+----------------------+-----------+---------------------+----------------+
| Cloud VR/AR Video |downstream | 50ms |no more than |
|comfortable experience|>140Mbps | |0.001% |
|full perspective | | | |
+----------------------+-----------+---------------------+----------------+
| Cloud VR/AR strong |downstream | 15ms |no more than |
|interaction |>260Mbps | |0.001% |
|comfortable experience| | | |
|I frame and P frame | | | |
+----------------------+-----------+---------------------+----------------+
| Cloud VR/AR strong |downstream | 8ms |no more than |
|interaction |1Gbps | |0.0001% |
|8K ideal experience | | | |
|I frame and P frame | | | |
+----------------------+-----------+---------------------+----------------+
Figure 5: The Requirements of Cloud VR/AR
3.2.1.2. Cloud Games
Cloud Game is an online gaming technology based on cloud computing
technology. Cloud gaming technology enables lightweight devices with
relatively limited graphics processing and data computing
capabilities to run high-quality games. In cloud game scenarios,
game related computing is not run on the user terminal, but on a
cloud server, which renders the game scene as a video and audio
stream and transmits it to the user terminal through the network.
The user's cloud gaming experience relies on a high-quality, low
latency network environment.
The following table describes requirements of Cloud Games:
+----------------------+-----------+---------------------+----------------+
| Requirement | Bandwidth |One-way maximum delay|Video resolution|
+----------------------+-----------+---------------------+----------------+
| Junior level | >8Mbps | 150ms |720P |
+----------------------+-----------+---------------------+----------------+
| 3A professional level| >12Mbps | 60ms |1080P |
+----------------------+-----------+---------------------+----------------+
| Level of esports | >40Mbps | 60ms |4K |
+----------------------+-----------+---------------------+----------------+
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Figure 6: Requirements of Cloud Games
3.2.1.3. Cloud Live Streaming
For scenarios such as concerts, press conferences, sports events, and
live events, cloud live streaming uses 5G uplink high bandwidth to
transmit 8K/VR videos. Combined with various applications such as
video analysis based on live streaming services, character and scene
recognition, real-time presentation of athlete and event data, and VR
live streaming interaction, it provides a brand new and rich event
viewing experience.
The following table describes requirements of Cloud live streaming:
+------------------------+---------------------+
| 8K live streaming | Attribute |
| 8K video feedback | |
+------------------------+---------------------+
| Bandwidth | upstream>100Mbps |
| | |
| One-way maximum delay | 200ms |
| | |
| Availability | 99.9% |
| | |
| Frame rate | 60 |
+------------------------+---------------------+
Figure 7: Requirements of Cloud Live Streaming
3.2.2. Requests to the IETF
* High requirements for video quality and transmission speed
* Cloud processing with real-time interaction
* Cloud-based deployment, which requires transmission through
heterogeneous networks
* No jitter requirements
* Packet loss is less than 0.001% or zero
* End-to-end delay requirements differ from applications and
services, such as 8ms, 15ms, 50ms, 150ms, 200ms and so on
3.3. Intelligent Computing
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3.3.1. Use Case Description
Intelligent computing refers to the integration of artificial
intelligence (AI) techniques with computational methods to enhance
the performance, efficiency, and capabilities of computing systems.
It involves the use of algorithms, machine learning models, and other
AI approaches to solve complex problems, analyze large datasets, and
improve decision-making processes. Intelligent Computing has
specific requirements for deterministic networks to ensure reliable
and predictable performance such as predictable latency, low packet
loss rate, high throughput and reliability. The typical scenarios
involve applications such as AI-based scientific research and
autonomous vehicles and so on.
3.3.1.1. Scientific Research
Intelligent computing is used to provide computing and data analysis
capabilities, which are crucial for handling large-scale scientific
simulations and datasets such as astronomy, climate science, and
bioinformatics. In scientific research, a large amount of computing
power resources such as CPU, GPU, memory, and other P-level or higher
are usually required. The network needs to provide services for data
volume of 10G to 100G or above, which requires high bandwidth, high
reliability and high throughput with ultra-low packet loss.
Many applications in scientific research, such as remote
observations, real-time data analysis, and distributed computing,
require networks to provide stable low latency and high reliability.
It must provide millisecond or even microsecond level latency and
jitter guarantees. For example, in nuclear fusion experiments, the
carrier network is required to have 99.999% availability.
3.3.1.2. Autonomous Vehicles
Intelligent computing is used in the development of self-driving
cars, which rely on AI algorithms for perception, decision-making,
and control. Autonomous vehicles refers to the technology of
vehicles that are capable of navigating without the need for human
input such as identifying other vehicles, pedestrians, and traffic
signals. It relies heavily on deterministic forwarding to ensure
safe, efficient, and reliable operation. It is also challenging for
big data management of autonomous driving. Vehicles record data from
4K HD cameras, laser scanners, and radars on the road. Each vehicle
can generate 80TB of data per day, which requires data-intensive
transmission.
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V2X (Vehicle-to-Everything) is a fundamental component of the
autonomous driving ecosystem, providing the necessary communication
backbone that enables vehicles to interact with their environment in
a safe and efficient manner. V2X provides the communication
infrastructure that enables vehicles to exchange information with
each other (V2V), with roadside infrastructure (V2I), with
pedestrians (V2P), and with the network (V2N). This exchange of
information is crucial for autonomous vehicles to make informed
decisions, improve navigation accuracy, and enhance overall road
safety. The following table describes requirements of 5G V2X which
is divided into four scenarios. (These metrics are based on 3GPP TR
22.886)
+----------------------+---------------------+--------------+
| Requirement | Communication Delay | Availability |
+----------------------+---------------------+--------------+
| Vehicles Platooning | 10~25ms | 99%~99.99% |
+----------------------+---------------------+--------------+
| Extended Sensors | 3~100ms | 99%~99.999% |
+----------------------+---------------------+--------------+
| Advanced Driving | 3~100ms | 99%~99.999% |
+----------------------+---------------------+--------------+
| Remote Driving | 5ms | 99.999% |
+----------------------+---------------------+--------------+
Figure 8: The Requirements of Autonomous Vehicles
3.3.2. Requests to the IETF
* Real-time communication
* Data-intensive transmission with high-throughput and ultra-low
packet loss
* Low bounded latency, such as us~ms
* High availability, such as 99.999%
3.4. ISAC-Enabled Smart Factory
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3.4.1. Use Case Description
A Smart Factory enabled by Integrated Sensing and Communication
(ISAC)-enabled cellular networks utilizes Radio Frequency (RF)
signals (aka Sensing Signals) to construct an environmental mapping,
detect and track objects, enable precise localization, and facilitate
collision avoidance for Autonomous Guided Vehicles (AGVs) and robotic
systems. ISAC systems encompass one or more Sensing Transmitters
(Tx) that transmit sensing signals and one or more Sensing Receivers
(Rx) that generate Sensing Data. Sensing Data are used in the
cellular network to describe the detected target objects in shape,
location, orientation, material, and spatial relationships among each
other. Sensing Data are then exposed to the Sensing Service Consumer
that requested them and are used for real-time monitoring and
decision-making by a Sensing Service Consumer. This reduces reliance
on dedicated sensors while optimizing communication resources.
Similar use cases have been considered in ETSI ISG ISAC. The
described workflow shown in Figure and illustrates a DetNet-enabled
cellular network as described in 3GPP TS 23.501, that contains core
network (CN) and Sensing Rxs, e.g., user equipment (UE) or base
station (BS), and a Sensing Service Consumer operating.
Sensing Sensing
+------------------+ Data +---------------------------------+ Data +---------------+
| Sensing Service +----------| Cellular Core Network +---------| Sensing Rx |
| | | | | (UE, BS) |
| Consumer | | | | |
+------------------+ | | +---------------+
+---------------------------------+
\___________________/ \___________________________________________________________/
DetNet-Enabled DetNet-Enabled
Data Network Cellular System
Figure 9: Sensing Rx in the smart factory generating Sensing Data
from the Sensing Signals and sending it to a a cellular core
network and Sensing Service Consumer for real-time decision
making
DetNet is critical for ensuring low-latency, bounded jitter, and
high-reliability exchange of Sensing Data between Sensing Rxs and the
network. The Sensing Data extracted from Sensing Signals at the
Sensing Rx must be delivered deterministically to enable accurate and
timely control of factory operations, such as predictive maintenance,
AGVs coordination, safety enforcement, and autonomous route planning
for AGVs.
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3.4.1.1. Predictive Maintenance
Predictive maintenance in a Smart Factory leverages ISAC to detect
early signs of equipment wear, misalignment, or failures by analyzing
environmental changes. The system can monitor machine vibrations,
structural integrity, and operational anomalies.
To enable real-time fault detection and proactive maintenance, the
network must support low-latency, high-reliability, and deterministic
data delivery to ensure timely analysis and decision-making. Delays
or packet loss in Sensing Data transmission can result in missed
failure indicators, leading to unplanned downtime and costly repairs.
+--------------+--------------------------------------------------+
| Requirement | Attributes |
+--------------+--------------------------------------------------+
|Bandwidth |10Mbps~1Gbps (depending on sensing resolution) |
+--------------+--------------------------------------------------+
|One-way delay |less than 5ms (for real-time anomaly detection) |
+--------------+--------------------------------------------------+
|Maximum jitter|less than 50us(to ensure stable data transmission)|
+--------------+--------------------------------------------------+
|Availability |99.999%(to prevent data loss and ensure |
| |continuous monitoring) |
+-----------------------------------------------------------------+
Figure 10: The Requirements of Predictive Maintenance
3.4.1.2. Real-Time Process Optimization
In a Smart Factory, real-time process optimization relies on sensing
to dynamically adjust production parameters, robotic operations, and
workflow scheduling based on real-time environmental and operational
data. Processed Sensing Data measured from Sensing Signals are used
to provide instantaneous feedback on equipment status, material flow,
and environmental conditions, enabling adaptive decision-making to
maximize efficiency and reduce downtime.
To ensure precise control and automation, the network must provide
ultra-low latency, deterministic jitter, and high availability to
support time-sensitive end-to-end data exchange between sensing
receivers and the cellular network and between the cellular network
and the control systems. Any delay or jitter in data transmission
can lead to inefficiencies, product defects, or production line
disruptions.
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+--------------+--------------------------------------------------+
| Requirement | Attributes |
+--------------+--------------------------------------------------+
|Bandwidth |100 Mbps~10 Gbps (depending on sensing resolution)|
+--------------+--------------------------------------------------+
|One-way delay |less than 1ms (for closed-loop process control) |
+--------------+--------------------------------------------------+
|Maximum jitter|less than 10us(for precise synchronization) |
+--------------+--------------------------------------------------+
|Availability |99.999% |
+-----------------------------------------------------------------+
Figure 11: The Requirements of Real-Time Process Optimization
3.4.1.3. Safety Control and Maintenance
Safety control in a Smart Factory relies on ISAC-enabled RF-based
sensing to detect potential hazards, such as worker proximity to
dangerous machinery, unexpected obstacles in AGV paths, or emergency
situations like fires or equipment failures. Unlike traditional
sensor-based systems, ISAC uses Sensing Signals (RF or non-RF) to
track moving objects, monitor workspaces, and trigger real-time
safety mechanisms without requiring additional sensing
infrastructure.
To ensure instantaneous hazard detection and response, the network
must support ultra-low latency, high availability, and deterministic
jitter in and end-to-end fashion to guarantee timely activation of
emergency protocols, such as stopping machines, rerouting AGVs, or
alerting human operators. Any delay or packet loss when exchanging
Sensing Data between Sensing Rxs and the cellular network or
exchanging Sensing Results between the cellular network and the
application could result in serious safety risks, including workplace
accidents and equipment damage.
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+--------------+--------------------------------------------------+
| Requirement | Attributes |
+--------------+--------------------------------------------------+
|Bandwidth |100 Mbps~10 Gbps (for real-time updates) |
+--------------+--------------------------------------------------+
|One-way delay |less than 1ms (for immediate hazard response) |
+--------------+--------------------------------------------------+
|Maximum jitter|less than 10us(for precise situation) |
+--------------+--------------------------------------------------+
|Availability |99.999999% |
+-----------------------------------------------------------------+
Figure 12: The Requirements of Real-Time Process Optimization
3.4.2. Requests to the IETF
To support Smart Factory ISAC use cases, the following enhancements
to DetNet are required:
* Ultra-low latency networking (as low as 1ms) for closed-loop
control and real-time process optimization.
* Stringent jitter requirements (as low as 10us) to support precise
sensing-based control.
* High bandwidth support (up to 10Gbps) for high-resolution sensing
data transmission.
* High availability (up to 99.999999%) to ensure robust industrial
operations.
DetNet should provide predictable and deterministic communication for
ISAC-enabled Smart Factories, ensuring timely and precise Sensing
Data delivery for industrial automation and control operations.
3.5. Interconnection of Time Sensitive Domains
3.5.1. Use Case Description
Some industrial production environments are basing their internal
communications on layer-2 Time Sensitive Networking. The
deterministic behavior is then constrained into the boundaries of the
factory domains.
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However, is can be of interest to interconnect such domains for
centralizing applications or functions relevant to the production
context. In order to do so, it is necessary to guarantee
deterministic behavior as well in the network used for
interconnecting such domains.
[5G-ACIA] describes some initial scenarios of DetNet and TSN
interworking. The purpose of this use case is to allow the practical
interconnection of such domains. The expectation is that the
interconnection of those domains handle the flows exiting the TSN
domains providing bounded latency and extremely low losses when
passing through the DetNet domain in a transparent manner.
3.5.2. Requests to the IETF
* Provide bounded latency for TSN flows
* Provide low packet losses, as low as the frame losses in TSN
4. Use Case Common Themes
4.1. Requirements with Deterministic Classification
The above applications differ in the network ranges and SLAs
requirements such as bounded latency, jitter, bandwidth, availability
and packet loss. The classification should consider the
characteristics such as traffic specification and service
requirements. The following table summarizes deterministic
requirements of industrial internet, cloud video and intelligent
computing applications, etc.
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+---+------------+--------------------+---------------------------------------------------------+
| | Use Cases | Typical | Differentiated Deterministic Requirements |
| | | Applications +----------+----------+---------+-------------------------+
| | | |Bandwidth | Delay | Jitter |Packet Loss| Availability|
+---+------------+--------------------+----------+----------+---------+-----------+-------------+
| 1 |Industrial |Machine Vision | Low | Low | N/A | N/A | Medium |
| |Internet +--------------------+----------+----------+---------+-----------+-------------+
| | |Remote Control | Low | Low |Ultra-low| N/A | High |
| | +--------------------+----------+----------+---------+-----------+-------------+
| | |AGV Control |Low~High |Low~Medium| N/A | N/A | Ultra-high |
| | +--------------------+----------+----------+---------+-----------+-------------+
| | |AR Assistance | Low | Low |Ultra-low| N/A | High |
+---+------------+--------------------+----------+----------+---------+-----------+-------------+
| 2 |High |Cloud VR and AR |Medium | Low | N/A | Ultra-low | N/A |
| |Experience | | ~High | | | or zero | |
| |Video +--------------------+----------+----------+---------+-----------+-------------+
| | |Cloud Games | Low | High | N/A | N/A | N/A |
| | +--------------------+----------+----------+---------+-----------+-------------+
| | |Cloud Live Streaming| Medium | High | N/A | N/A | Medium |
+---+------------+--------------------+----------+----------+---------+-----------+-------------+
| 3 |Intelligent |Scientific Research |Ultra-high| Low | N/A | Ultra-low | Ultra-high |
| |Computing | | | | | or zero | |
| | +--------------------+----------+----------+---------+-----------+-------------+
| | |Autonomous Vehicles |Ultra-high| Low | N/A | Ultra-low | Ultra-high |
| | | | | | | or zero | |
+---+------------+--------------------+----------+----------+---------+-----------+-------------+
Figure 13: Characteristics of Typical Applications
Since the DetNet applications differ in their requirements, it
demands specific desired deterministic behaviors. The flow
aggregation based on the classification of deterministic services
should be taken into considerations as discussed in
[I-D.xiong-detnet-flow-aggregation]. It is required to provide
latency, bounded jitter and packet loss dynamically and flexibly in
all scenarios for each characterized flow.
4.2. Requirements with Ultra-low or Zero Packet Loss
Some high-throughput, low-latency applications applications such as
intelligent computing demand ultra-low packet loss which is critical
to ensure real-time data processing, maintain data integrity,
optimize resource utilization, and support scalable and reliable
operations. And some applications such as AR/VR do not fit as
payload into a single IP packet and may be fragmented into multiple
smaller chunks as discussed in [I-D.rc-detnet-data-unit-groups]. It
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demands zero packet loss for some chunks while a single packet loss
can lead to the loss of the whole application. The DetNet node
should provide the deterministic behavior to perform any DetNet
queuing, shaping, scheduling, ordering or dropping to guarantee the
packet loss on particular packets.
5. Security Considerations
Security considerations for DetNet are covered in the DetNet
Architecture [RFC8655] and DetNet use cases [RFC8578] and DetNet
security considerations [RFC9055].
6. IANA Considerations
This document makes no requests for IANA action.
7. Acknowledgements
The authors would like to acknowledge Aihua Liu and Bin Tan for their
thorough review and very helpful comments.
8. References
8.1. Informative References
[I-D.ietf-detnet-scaling-requirements]
Liu, P., Li, Y., Eckert, T. T., Xiong, Q., Ryoo, J.,
zhushiyin, and X. Geng, "Requirements for Scaling
Deterministic Networks", Work in Progress, Internet-Draft,
draft-ietf-detnet-scaling-requirements-07, 20 November
2024, <https://datatracker.ietf.org/doc/html/draft-ietf-
detnet-scaling-requirements-07>.
[I-D.rc-detnet-data-unit-groups]
Robitzsch, S. and L. M. Contreras, "Data Unit Groups for
DetNet-Enabled Networks", Work in Progress, Internet-
Draft, draft-rc-detnet-data-unit-groups-00, 21 October
2024, <https://datatracker.ietf.org/doc/html/draft-rc-
detnet-data-unit-groups-00>.
[I-D.xiong-detnet-flow-aggregation]
Xiong, Q., Jiang, T., and J. Joung, "Flow Aggregation for
Enhanced DetNet", Work in Progress, Internet-Draft, draft-
xiong-detnet-flow-aggregation-02, 25 February 2025,
<https://datatracker.ietf.org/doc/html/draft-xiong-detnet-
flow-aggregation-02>.
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[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", BCP 14, RFC 2119,
DOI 10.17487/RFC2119, March 1997,
<https://www.rfc-editor.org/info/rfc2119>.
[RFC8174] Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC
2119 Key Words", BCP 14, RFC 8174, DOI 10.17487/RFC8174,
May 2017, <https://www.rfc-editor.org/info/rfc8174>.
[RFC8578] Grossman, E., Ed., "Deterministic Networking Use Cases",
RFC 8578, DOI 10.17487/RFC8578, May 2019,
<https://www.rfc-editor.org/info/rfc8578>.
[RFC8655] Finn, N., Thubert, P., Varga, B., and J. Farkas,
"Deterministic Networking Architecture", RFC 8655,
DOI 10.17487/RFC8655, October 2019,
<https://www.rfc-editor.org/info/rfc8655>.
[RFC8664] Sivabalan, S., Filsfils, C., Tantsura, J., Henderickx, W.,
and J. Hardwick, "Path Computation Element Communication
Protocol (PCEP) Extensions for Segment Routing", RFC 8664,
DOI 10.17487/RFC8664, December 2019,
<https://www.rfc-editor.org/info/rfc8664>.
[RFC9055] Grossman, E., Ed., Mizrahi, T., and A. Hacker,
"Deterministic Networking (DetNet) Security
Considerations", RFC 9055, DOI 10.17487/RFC9055, June
2021, <https://www.rfc-editor.org/info/rfc9055>.
[RFC9320] Finn, N., Le Boudec, J.-Y., Mohammadpour, E., Zhang, J.,
and B. Varga, "Deterministic Networking (DetNet) Bounded
Latency", RFC 9320, DOI 10.17487/RFC9320, November 2022,
<https://www.rfc-editor.org/info/rfc9320>.
Authors' Addresses
Junfeng Zhao
CAICT
China
Email: zhaojunfeng@caict.ac.cn
Quan Xiong
ZTE Corporation
China
Email: xiong.quan@zte.com.cn
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Zongpeng Du
China Mobile
China
Email: duzongpeng@chinamobile.com
Muhammad Awais Jadoon
InterDigital
Email: Muhammad.AwaisJadoon@InterDigital.com
Luis M. Contreras
InterDigital
Email: luismiguel.contrerasmurillo@telefonica.com
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