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Industrial Internet of Things                                    C. Tang
Internet-Draft                                      Chongqing University
Intended status: Informational                                   S. Ruan
Expires: 23 May 2021                                            B. Huang
                                                                  H. Wen
                                                                 X. Feng
                                                    ChongQing University
                                                        19 November 2020


     Research on multipriority scheduling technology for real-time
  interconnection between industrial field data and cloud information
                draft-tang-iiot-industrial-scheduling-03

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.

Status of This Memo

   This Internet-Draft is submitted in full conformance with the
   provisions of BCP 78 and BCP 79.

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   This Internet-Draft will expire on 23 May 2021.

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   Copyright (c) 2020 IETF Trust and the persons identified as the
   document authors.  All rights reserved.

   This document is subject to BCP 78 and the IETF Trust's Legal
   Provisions Relating to IETF Documents (https://trustee.ietf.org/
   license-info) in effect on the date of publication of this document.



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   Please review these documents carefully, as they describe your rights
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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
     1.1.  Application requirements of industrial Internet . . . . .   3
       1.1.1.  Data collection of Internet of Things . . . . . . . .   3
       1.1.2.  Intelligent robots  . . . . . . . . . . . . . . . . .   4
       1.1.3.  Industrial AR . . . . . . . . . . . . . . . . . . . .   4
       1.1.4.  Other business needs  . . . . . . . . . . . . . . . .   4
   2.  Terminology . . . . . . . . . . . . . . . . . . . . . . . . .   4
   3.  Uplink scheduling scheme based on 5G slice  . . . . . . . . .   5
     3.1.  Upline scheduling process of 5G NR  . . . . . . . . . . .   5
     3.2.  Uplink scheduling scheme flow . . . . . . . . . . . . . .   6
     3.3.  Interslice scheduling . . . . . . . . . . . . . . . . . .   6
     3.4.  Intraslice user scheduling  . . . . . . . . . . . . . . .   8
     3.5.  Summary of this chapter . . . . . . . . . . . . . . . . .   9
   4.  Collaborative scheduling algorithm based on edge computing for
           big data tasks in the industrial field  . . . . . . . . .  10
     4.1.  Flow of collaborative task scheduling algorithm . . . . .  11
     4.2.  Scheduling strategy process . . . . . . . . . . . . . . .  12
     4.3.  Summary of this chapter . . . . . . . . . . . . . . . . .  12
   5.  Security Considerations . . . . . . . . . . . . . . . . . . .  13
     5.1.  Physical security requirements  . . . . . . . . . . . . .  13
     5.2.  Network security requirements . . . . . . . . . . . . . .  13
     5.3.  Data security requirements  . . . . . . . . . . . . . . .  13
     5.4.  Application of security requirements  . . . . . . . . . .  14
   6.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .  14
   7.  Acknowledgments . . . . . . . . . . . . . . . . . . . . . . .  14
   8.  References  . . . . . . . . . . . . . . . . . . . . . . . . .  14
     8.1.  Normative References  . . . . . . . . . . . . . . . . . .  14
     8.2.  Informative References  . . . . . . . . . . . . . . . . .  14
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  15

1.  Introduction

   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



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   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
   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.  Application requirements of industrial Internet

1.1.1.  Data collection of Internet of Things

   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



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   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.  Intelligent robots

   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.  Industrial AR

   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.

1.1.4.  Other business needs

   The industrial field has other business needs, including security
   monitoring.  Different businesses have different requirements for
   network services.

2.  Terminology

   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.










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3.  Uplink scheduling scheme based on 5G slice

   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.  Upline scheduling process of 5G NR

   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
   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.





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   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.  Uplink scheduling scheme flow

   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.

   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.  Interslice scheduling

   For Step 2 of the uplink scheduling scheme based on 5G slice,
   interslice resource scheduling needs to meet the following:



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   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):

                 P(i,j) = R(i,j)                                (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.

   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.



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   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.  Intraslice user scheduling

   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, j) = -log(p(i))/Td(i) * r(i, j)/R(i) * d(i)/D  (2)

















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   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
   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.  Summary of this chapter

   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.








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   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.

4.  Collaborative scheduling algorithm based on edge computing for big
    data tasks in the industrial field

   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.




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   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
   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.  Flow of collaborative task scheduling algorithm

   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:






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   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.

   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.  Scheduling strategy process

   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.  Summary of this chapter

   Relative to the existing task resource collaborative scheduling
   algorithm in the industrial field, the algorithm proposed in this
   work has the following advantages:




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   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
   the construction of new optimization goals to achieve system resource
   utilization efficiency and user satisfaction.

5.  Security Considerations

5.1.  Physical security requirements

   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.  Network security requirements

   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.  Data security requirements

   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



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   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.

5.4.  Application of security requirements

   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.  IANA Considerations

   This memo includes no request to IANA.

7.  Acknowledgments

   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

8.1.  Normative References

   [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>.

8.2.  Informative References






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   [Delay-optimal]
              Liu, J. and Y. Zhang, "Delay-optimal computation task
              scheduling for mobile-edge computing systems", IEEE
              International Symposium on Information Theory
              (ISIT) pp.1451-1455, July 2016.

   [Energy-Efficient]
              Wang, J. and J. Tang, "Towards energy-efficient task
              scheduling on smartphones in mobile crowd sensing
              systems", Computer Networks pp.115,100-109, 2017.

   [QoSBased-Distribution]
              Song, Y. and S.S. Yau, "An approach to QoS-based task
              distribution in edge computing networks for IoT
              applications", IEEE international conference on edge
              computing (EDGE) pp.32-39, July 2016.

   [Scheduling-Latency-Sensitive]
              Scoca, V. and A. Brandic, "Scheduling Latency-Sensitive
              Applications in Edge Computing", In Closer. pp.158-168,
              Scheduling Latency-Sensitive , 2018.

   [Spanedge] Sajjad, H. P. and K. Danniswara, "Spanedge: Towards
              unifying stream processing over central and near-the-edge
              data centers", IEEE/ACM Symposium on Edge Computing
              (SEC) pp. 168-178, October 2016.

Authors' Addresses

   Chaowei Tang
   ChongQing University
   No.174 Shazheng Street, Shapingba District
   Chongqing
   400044
   China

   Email: cwtang@cqu.edu.cn


   Ruan Shuai
   ChongQing University
   No.174 Shazheng Street, Shapingba District
   ChongQing
   China

   Phone: +86 189-6826-0296
   Email: rs@cqu.edu.cn




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   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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