Internet DRAFT - draft-tang-iiot-industrial-scheduling
draft-tang-iiot-industrial-scheduling
Industrial Internet of Things C. Tang
Internet-Draft Chongqing University
Intended status: Informational S. Ruan
Expires: November 20, 2021 B. Huang
H. Wen
X. Feng
ChongQing University
May 19, 2021
Research on multipriority scheduling technology for real-time
interconnection between industrial field data and cloud information
draft-tang-iiot-industrial-scheduling-04
Abstract
This document describes the multipriority scheduling technology for
the interconnection between industrial field and cloud data in the
application of 5G communication. The technology includes spectrum
resource scheduling based on 5G slice in the process of accessing
industrial data and task collaborative scheduling based on edge
computing.
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1.
The rapid development of 5G mobile communication is driven by
different application scenarios and diversified service deployment.
Industrial Internet based on 5G technology has also accelerated
research and deployment. In maximizing the role of 5G technology in
the industrial system, the priority is to realize the interconnection
between the industrial site and the cloud. On the one hand, the
spectrum resources for 5G access are limited; on the other hand, the
constraints of industrial equipment computing resources prompt
factories to unload a portion of their industrial applications to
computing systems with sufficient computing resources, such as
microservers, cloud servers, or data centers. Therefore, the
multipriority scheduling between industrial factory data and cloud
computing is an important issue to be solved.
In the industrial environment, industrial factory data mainly refer
to the real-time data generated by industrial production equipment
and target products under the operation mode of the Internet of
Things. These data include the those reflecting the operation state
of equipment and products, such as the operation and operation
conditions, working conditions, and environmental parameters. These
data can be uploaded to the cloud for data processing and analysis
through 5G base stations. The data can then be reused by factories
for intelligent design, intelligent production, networked
collaborative manufacturing, intelligent service, and personalized
customization.
In the future smart factories, the demand for industrial field
applications, including Internet of Things data acquisition,
intelligent robots, industrial augmented reality (AR), and other
services, is expected to increase. As applications have different
demands for network service quality，multipriority scheduling
technology should be studied on the basis of service quality.
From the industrial factory to the cloud computing center, the
priority scheduling problem can be decomposed into two parts. The
first part includes the allocation of spectrum resources
corresponding to the schedule for different priority services in the
process of industrial data access through 5G communication
technology. For this purpose, we propose an uplink scheduling scheme
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for industrial field data based on 5G slice. The other part includes
the allocation and scheduling of computing resources for tasks in
edge computing nodes and cloud computing nodes. For this purpose, we
propose a collaborative scheduling scheme for big data tasks in
industrial fields based on edge computing.
1.1.
1.1.1.
The Internet of Things is the Internet of everything. The Internet
of Things data contain all types of essential information, such as
sound, light, heat, electricity, mechanics, chemistry, biology, and
location. Its goal is to combine all types of information from
sensing devices with the Internet to realize the interconnection of
people, machines, and objects at any time and place. Therefore,
massive machine communication brings great demand for network
coverage. The information collection of industrial control systems
includes the sensor data collection designed in these industrial
systems. These systems mainly collect the physical events and data
generated in industrial production and manufacturing factories,
including various physical quantities, identification, positioning,
and other data.
1.1.2.
Machine vision has become increasingly popular in manufacturing
enterprises, such as automobile factories, because of its
effectiveness in detecting product defects. This type of application
requires a large network bandwidth. As intelligent robots require
complete corresponding intelligent operations, the fast response of a
highly reliable 5G network is also a prerequisite.
1.1.3.
5G and AR are projected to become important applications of the
Industrial Internet. The combination of 5G and AR can be applied to
multiple scenes in industrial factories, including man-machine
collaboration, monitoring of production processes, pre-job training
for new employees, product quality detection, and remote assistance
and guidance. For example, when industrial equipment is damaged and
needs to be repaired, remote technicians can control the robot
remotely through AR to complete the maintenance process. In such a
case, the industrial network needs to provide a reliable network
bandwidth and address low latency communication requirements.
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1.1.4.
The industrial field has other business needs, including security
monitoring. Different businesses have different requirements for
network services.
2.
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 ERROR: Undefined
target: RFC2119.
3.
In the access stage from the industrial field to 5G New Radio (NR),
the base station side needs to perform multipriority uplink
scheduling for different service data and allocate spectrum resources
because of the service demand and communication quality of different
data on site.
In this work, interslice scheduling and intraslice user scheduling
are used for uplink resource allocation. In terms of satisfying the
service quality required by different 5G slice scenarios and
different industrial field data, it can effectively improve the
fairness and throughput of scheduling.
3.1.
First, when UE needs to send uplink data, it puts the required data
into the cache and then submits its buffer state report to the base
station through the physical uplink control channel. At the same
time, the scheduling request is sent to inform the base station gNB
(5G base station) that it needs to send data.
Second, the uplink scheduler of gNB receives an uplink scheduling
request from UE, and gNB allocates resources to UE on the basis of
UE's cache status report and the uplink channel condition of UE. The
uplink channel status of UE is obtained by the sounding reference
signal that UE periodically sends to gNB. The allocation results are
sent to UE via the physical downlink control channel by using the
uplink grant.
Third, UE uses the resources allocated by the base station to send
data to the base station through the physical uplink shared channel.
The uplink scheduler of gNB receives the cache status report and
upline channel status of UE and completes the dynamic scheduling of
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time-frequency resources according to the built-in scheduling
algorithm. There are three common scheduling algorithms: Round-
Robin(RR) algorithm, Max C / Ι algorithm, Proportional
Fairness(PF) algorithm.
The uplink scheduler of gNB receives the cache status report and the
upline channel status of UE and completes the dynamic scheduling of
time-frequency resources according to the built-in scheduling
algorithm. The three most common scheduling algorithms are the round
robin (RR) algorithm, max C/Ι algorithm, and proportional
fairness(PF) algorithm.
The RR algorithm allocates resources for different users of request
scheduling in a circular way. This algorithm only considers the
fairness among users and loses the system throughput. The max C/I
algorithm always provides resources for the best users of the
channel. It can maximize the system throughput, but it cannot
guarantee the fairness between cell users. The PF algorithm
considers the ratio of instantaneous rate and long-term average rate
when selecting users. It adjusts different users by using weight
values to achieve the purpose of consideration of the overall
throughput of the system and fairness of users. However, it does not
consider quality of service (QoS) information.
The explosive growth of data rate and capacity demand, as well as the
large-scale, high reliability, low delay, and other differentiated
demands, have brought about the development of 5G. Therefore, in the
face of different industrial scenarios and different QoS requirements
of businesses, a highly reasonable multipriority scheduling scheme
needs to be designed. Under the condition of limited wireless
resources, a reasonable scheme allocates wireless resources for
different 5G slices to meet the service requirements of high-priority
services in intelligent manufacturing plants and improve the resource
utilization rate and fairness among users as much as possible.
This section presents a multipriority resource scheduling method for
industrial field data to improve resource utilization as much as
possible and thereby meet the service quality required by different
industrial field services under different 5G slice scenarios.
3.2.
The general flow of the uplink scheduling scheme based on 5G slice is
as follows:
Step 1: During a scheduling cycle, determine if the task cache queue
is empty. If it is null, then wait for the next scheduling cycle;
otherwise, proceed to the next step.
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Step 2: Use the interslice scheduling algorithm to allocate resources
to the three network slices according to requirements.
Step 3: For the resources obtained from each slice, perform resource
scheduling for each user in the slice. Upon completion, wait for the
next scheduling cycle.
3.3.
For Step 2 of the uplink scheduling scheme based on 5G slice,
interslice resource scheduling needs to meet the following:
1. The resources obtained from different 5G slices are isolated and
independent in the frequency domain, and they can be adjusted
flexibly. The congestion of one 5G network slice does not affect the
other 5G network slices.
2. By allocating spectrum resources with good channel conditions to
a high-priority slice, the throughput of the system and the service
guarantee for high-priority services can be improved.
Assume that the number of resource blocks (RBs) that the scheduler
can configure is "Q" and that at the time t of scheduling, the total
number of users requesting resources is N. In this work, the
priority of each slice in each RB is defined in formula (1):
where P(i,j) represents the priority of the i-th slice at the j-th RB
in one scheduling cycle. The greater P(i,j) is, the higher the
scheduling priority of the i-th user in the j-th RB will be. In
improving the system throughput, the priority is based on the rate
and calculation of all users in the j-th RB in the i-th slice.
R(i,j) represents the rate sum of all users in the j-th RB in the
i-th slice. According to the calculation, we can obtain the priority
matrix.
For the service requirements of different businesses using industrial
field data, the uRLLC slice needs low delay and high reliability,
such as those exhibited by a real-time remote cooperative robot,
which needs to prioritize the allocation of resources and reduce
queuing delay. The eMMB slice has high data volume business
requirements. Hence, the priority allocation of resources will
improve the overall network throughput. However, its delay
requirements are not as high as those of the uRLLC slice. The mMTC
slice has the lowest scheduling priority, and in most cases, the
amount of uplink data is not large, and the delay requirement is low.
Therefore, given the order of the uRLLC slice, eMMB slice, and mMTC
slice, RB resources are configured according to the priority matrix.
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According to the resource scheduling requirements of slices, the
final interslice resource scheduling scheme is as follows:
Step 1: Calculate the priority matrix according to formula (1).
Step 2: According to the priority order of slices, select the i-th
slice for resource scheduling, and initialize i to 1.
Step 3: Select the i-th slice and the j-th RB with priority ranking,
and initialize j to 1.
Step 4: Determine whether the currently scheduled RB is adjacent to
the RB allocated by the slice. If yes, then perform step 5.
Otherwise, set j = j + 1 and repeat step 3.
Step 5: Assign priority j-th RB to the i-th slice and remove the RB
from the RB sequence.
Step 6: Determine whether the resource request for the i-th slice has
obtained enough resources. If so, then perform step 7. Otherwise,
set j = j + 1 and repeat step 3.
Step 7: Determine whether the RB sequence is empty or whether all
slices have obtained sufficient resources. If so, then end slice
resource scheduling. Otherwise, execute i = i + 1 and repeat step 2.
3.4.
After resource scheduling among slices, the slices obtain their
respective continuous and isolated RB groups. It does not interfere
with the intraslice user scheduling.
The user scheduling in the slice can be understood as a logical cell,
and the scheduler conducts resource scheduling on the users belonging
to this cell through the resources obtained by intraslice scheduling.
Given the different QoS requirements of data in the 5G industrial
field, the performance indicators that need to be comprehensively
considered in the process of user scheduling in the slice include
transmission rate, delay demand, packet loss rate, and the amount of
data to be transmitted.
The priority of user i in the j-th RB group at time t is calculated
by formula (2):
p(i) represents the maximum rate of packet loss of user i, i.e., p(i)
in (0, 1). Therefore, -log(p(i)) indicates that the lower the
maximum packet loss rate is, the higher the priority of user will be.
Td(i) represents the maximum wait delay for user i. The smaller
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Td(i) is, the higher the user priority will be. r(i, j) represents
the instantaneous transfer rate of the i-th user in the j-th RB group
during the t scheduling cycle. R(i) represents the average
transmission rate before the i-th user. The higher the instantaneous
transmission rate is, the better the channel quality condition is,
and the higher the priority is. d(i) represents the amount of data
that user i is waiting to send at time t. The higher the value of
d(i)/D is, the higher the proportion of the pending business volume
of user i in the total business volume of all requesting users at
time tis, and the higher the priority is.
Therefore, the intraslice user scheduling scheme is as follows:
Step 1: Complete the interslice scheduling.
Step 2: For all RBs of a single slice, they are divided into RB
groups of the same size according to the number of slice users.
Step 3: Calculate the priority of each user in the slice on each RB
group according to formula 2.
Step 4: Assign the RB group with the highest priority to each user in
turn according to user priority.
Step 5: Determine whether the RB sequence is empty. If so, then end
the resource allocation. Otherwise, repeat step 4.
3.5.
The scheme proposed in this section has the following advantages:
1. It analyzes the different service requirements for industrial
field data in a 5G environment. The interslice scheduling scheme is
used to complete the resource allocation of three 5G slices to ensure
the flexible scheduling and isolation of resources between slices.
The scheme also ensures that the required resources for high-priority
businesses, such as the uRLLC slice business, are allocated and
improve the throughput of the system.
2. The intraslice scheduling comprehensively considers the data
service transmission rate, delay demand, packet loss rate, data
volume to be transmitted, and other performance indicators. It also
gives priority to the scheduling of users with good channel
conditions, high delay requirements, high-reliability requirements,
and large data volume to be sent.
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4.
With the rapid development of industrial Internet and mobile
communication technology, applications such as face recognition,
short video traffic, autonomous driving, drone operations, industrial
detection, and others have high requirements for computing. However,
relying only on the current centralized cloud computing architecture
model does not meet the required computing power of businesses. Amid
the continuous generation of big data in industrial production,
entertainment, education, and other industries, cloud-centric
computing architecture should rely on new distributed computing
architecture, such as edge and fog computing, to alleviate
computational stress.
Correspondingly, the emergence of big data poses a challenge to the
improvement of the performance of end devices. According to the type
of data and service quality requirements, higher requirements are put
forward for computing speed and processing capacity. Increased
endpoint computing power also provides a good boost to distributed
computing architecture. For example, some tasks with particularly
high latency requirements are suitable for distributed processing
mechanisms with the help of high-performance end devices because by
relying only on cloud processing, real-time performance cannot be met
under heavy network load. Therefore, edge computing needs to be
utilized to sink computing power and dynamically allocate computing
resources on the basis of tasks and real-time performance. We indeed
know the importance of high computing power and effectively
distributed computing architecture. However, a number of challenges
exist in the industrial field environment; these challenges include
varied sensor data, the corresponding instruction requirements
generated by devices, and service processing. To a certain extent,
the computing power of field devices is insufficient as well, and
data present complex characteristics. Relying solely on edge
terminal equipment to implement business logic is difficult. In
complex industrial sites, the response requirements of various
services are inconsistent. Thus, the system's response capabilities,
processing capabilities, and throughput capabilities are put to the
test. Thus, end devices and servers should be combined to achieve
good collaboration.
Therefore, in the industrial Internet big data scenario, the
collaboration of tasks, the allocation of resources, and the
efficient processing of data offer a huge research space and
important practical value, thereby attracting the attention of many
scholars. However, the traditional research point focuses on
resource allocation and the utilization of edge nodes, the
coordinated scheduling of the edge and the cloud, and the issue of
task priority. However, these algorithms have drawbacks. For
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example, they consider either the resource allocation problem between
nodes or the task priority problem, and they do not finish the
coordinated consideration. Moreover, the network link bandwidth
exerts an impact on the system. Hence, an improved task scheduling
algorithm should be proposed on the basis of task requirements, along
with edge computing, network bandwidth, and real-time task
requirements, to maximize resource utilization and user satisfaction.
4.1.
On the basis of the complex industrial site environment, task
requirements, and the different amounts of calculation, we take on a
new perspective as we comprehensively consider computing resources
and user satisfaction through edge computing technology. The goal is
to perform tasks between the terminal and edge server resource
scheduling problem. In the case of meeting the minimum resource
requirements, user satisfaction can also be guaranteed to meet the
real-time task processing generated in a variety of industrial
production processes.
According to the actual needs of industrial field task data, we
considered and provided a collaborative scheduling algorithm for
industrial field big data tasks based on edge computing. The steps
are as follows: the terminal publishes the service, and the scheduler
obtains the service information and calculates the number of tasks
and delay requirements. According to the task delay requirements, a
task's urgency and priority are evaluated. Subsequently, whether the
task should be offloaded to the edge server is determined according
to the current bandwidth resources. Meanwhile, the task cache status
of the scheduler and the computing information of each edge server
are obtained. On the basis of the system resource status, number of
queued processes, current business task volume, delay requirements,
etc., the reasonable task scheduling of the server and terminal is
performed. The process is repeated until all tasks are allocated and
executed.
The specific steps are as follows:
Step 1: The terminal publishes the service, and the scheduler obtains
the service information, such as the number of calculation tasks N(i)
and the delay requirements T(i) (i with (1,N)).
Step 2: The task priority LEVEL(i) (i with (1,N)) is evaluated
according to the time delay requirements of the task. It exerts an
impact on the subsequent task scheduling and further reflects user
satisfaction.
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Step 3: The current network link information, that is, the remaining
bandwidth of the network R, is obtained. Assuming that the upload
rate of the task is α, the task upload needs to meet. If the
current bandwidth is not enough, then the task needs to be unloaded,
and the waiting delay is recorded.
Step 4: The task scheduling threshold of the scheduler is obtained,
along with the calculation information of each edge server, the
number of tasks queued, and the queue's waiting delay.
Step 5: The reasonable task scheduling of the server and terminal is
performed according to the state of system resources, current
business task volume, and delay requirements.
4.2.
The following is the processing flow of the scheduling strategy.
We consider the task delay requirements, the remaining capacity of
the terminal, the remaining computing capacity of the edge server,
and the total delay of the task assigned to the terminal as the input
of the machine learning algorithm. The results of the former
calculation are then fed to the fully connected network layer, and
the output layer is maximized through the fully connected layer. The
softmax layer estimates the probability of assigning to the terminal
or the edge server. Therefore, the internal parameters of the
network are learnable parameters so that it can perform adaptive
adjustment to provide a basis for subsequent optimization on the
basis of the customized loss, system resource conditions, network
load, and optimization goals. The number of iterations can be
determined accordingly. The updated parameters are used to allocate
real resources to the tasks in the current scheduler.
4.3.
Relative to the existing task resource collaborative scheduling
algorithm in the industrial field, the algorithm proposed in this
work has the following advantages:
It can realize the reasonable scheduling of industrial field tasks in
terminal equipment and edge servers, fully consider the system's
computing resources, improve the system's ability to process tasks,
and minimize the resource consumption of the system. It can also
avoid calculation delay caused by an unbalanced resource allocation.
The algorithm considers the execution status of the system, the task
calculation amount, and the delay requirements for optimal scheduling
when performing task scheduling. It also comprehensively considers
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the construction of new optimization goals to achieve system resource
utilization efficiency and user satisfaction.
5.
5.1.
For edge computing equipment, security problems are caused by
indirect or self-inflicted causes during operation (e.g., energy
supply, cooling and dust removal, and equipment loss). Although
threats to operations are not as devastating as the damage caused by
natural disasters, the lack of a good response will still lead to
disastrous consequences, resulting in the performance degradation of
edge computing, service interruption, and data loss. Particularly in
the Industrial Internet scene, factories conduct sophisticated
equipment maintenance and overhaul, but dealing with the operation
and maintenance of IT equipment timely is difficult.
5.2.
Relative to cloud computing data centers, edge nodes have limited
capabilities and are highly vulnerable to hackers. The damage of a
single edge node is not extensive, and the network can quickly find
alternative nodes nearby. However, if hackers use the compromised
edge nodes as "broilers" to attack other servers, then they could
affect the entire network. Most existing security protection
technologies have complex computational protection processes, which
are not suitable for edge computing scenarios. Therefore, an
important network security requirement is to design lightweight
security technology suitable for edge computing architecture in the
Industrial Internet scene.
5.3.
In edge computing, users outsource data to edge nodes and transfer
the control of data to them. The process introduces the same
security threats as cloud computing. First, ensuring the
confidentiality and integrity of data is difficult because the
outsourced data may be lost or modified incorrectly. Second,
unauthorized parties may misuse the uploaded data to seek other
benefits. Relative to the cloud, edge computing avoids the long-
distance transmission of multiple routes and greatly reduces the
outsourcing risk. Therefore, the security problem of data belonging
to edge computing is increasingly prominent. For example, in such a
complex and changeable environment, the safe and rapid migration of
data after the collapse of an edge node should be realized.
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5.4.
Application security, as the name implies, guarantees the security of
application processes and results. In the era of marginal big data
processing, applications can be guaranteed to get short response
times and high reliability by moving application services from cloud
computing centers to network edge nodes. Meanwhile, network
transmission bandwidth and intelligent terminal power consumption can
be greatly reduced. However, edge computing suffers from common
application security problems in information systems, such as the
denial of service attack, unauthorized access, software
vulnerability, abuse of authority, and identity impersonation.
Moreover, it has other application security requirements because of
its characteristics. In the scenario in which multiple security
domains and access networks coexist at the edge, managing user
identity and realizing authorized access to resources become
important in ensuring application security.
6.
This memo includes no request to IANA.
7.
We thank all the contributors and reviewers and are deeply grateful
for the valuable comments offered by the chairpersons to improve this
draft.
8. References
Authors' Addresses
Chaowei Tang
ChongQing University
No.174 Shazheng Street, Shapingba District
Chongqing 400044
China
Email: cwtang@cqu.edu.cn
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Ruan Shuai
ChongQing University
No.174 Shazheng Street, Shapingba District
ChongQing
China
Phone: +86 189-6826-0296
Email: rs@cqu.edu.cn
Huang Baojin
ChongQing University
No.174 Shazheng Street, Shapingba District
ChongQing
China
Email: baojing-huang@foxmail.com
Wen Haotian
ChongQing University
No.174 Shazheng Street, Shapingba District
ChongQing
China
Email: wenhaotianrye@foxmail.com
Feng Xinxin
ChongQing University
No.174 Shazheng Street, Shapingba District
ChongQing
China
Email: xxfeng@cqu.edu.cn
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