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Research on Multi-Priority Scheduling Technology for Industrial Field and Cloud Interconnection
draft-tang-iiot-industrial-scheduling-01

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This is an older version of an Internet-Draft whose latest revision state is "Expired".
Authors Chaowei Tang , Ruan Shuai , Huang Baojin , Wen Haotian , Feng Xinxin
Last updated 2020-11-02
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draft-tang-iiot-industrial-scheduling-01
Industrial Internet of Things                                    C. Tang
Internet-Draft                                      Chongqing University
Intended status: Informational                                   S. Ruan
Expires: 6 May 2021                                             B. Huang
                                                                  H. Wen
                                                                 X. Feng
                                                    ChongQing University
                                                         2 November 2020

 Research on Multi-Priority Scheduling Technology for Industrial Field
                       and Cloud Interconnection
                draft-tang-iiot-industrial-scheduling-01

Abstract

   This document describes the multi-priority scheduling technology of
   industrial field and cloud interconnection under the application of
   5G communication, including spectrum resource scheduling based on 5G
   slice in the access process of 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.

   Internet-Drafts are working documents of the Internet Engineering
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   This Internet-Draft will expire on 6 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/
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   Please review these documents carefully, as they describe your rights

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   and restrictions with respect to this document.  Code Components
   extracted from this document must include Simplified BSD License text
   as described in Section 4.e of the Trust Legal Provisions and are
   provided without warranty as described in the Simplified BSD License.

Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
     1.1.  Application requirements of industrial Internet . . . . .   3
       1.1.1.  Internet of Things data collection  . . . . . . . . .   3
       1.1.2.  Intelligent robot . . . . . . . . . . . . . . . . . .   4
       1.1.3.  Industrial AR . . . . . . . . . . . . . . . . . . . .   4
       1.1.4.  Other businesses  . . . . . . . . . . . . . . . . . .   4
   2.  Terminology . . . . . . . . . . . . . . . . . . . . . . . . .   4
   3.  Uplink scheduling algorithm based on 5G slice . . . . . . . .   5
     3.1.  Common Upline scheduling of 5G NR:  . . . . . . . . . . .   5
     3.2.  Uplink scheduling algorithm flow  . . . . . . . . . . . .   6
     3.3.  Inter-slice scheduling  . . . . . . . . . . . . . . . . .   7
     3.4.  Intra-slice user scheduling . . . . . . . . . . . . . . .   8
     3.5.  The summary of this chapter . . . . . . . . . . . . . . .   9
   4.  Collaborative scheduling algorithm based on edge computing for
           big data task in industrial field . . . . . . . . . . . .  10
     4.1.  Task collaborative scheduling algorithm flow  . . . . . .  11
     4.2.  Scheduling strategy process . . . . . . . . . . . . . . .  12
     4.3.  The summary of this chapter . . . . . . . . . . . . . . .  12
   5.  Security Considerations . . . . . . . . . . . . . . . . . . .  13
     5.1.  Security Considerations . . . . . . . . . . . . . . . . .  13
     5.2.  Network security requirements . . . . . . . . . . . . . .  13
     5.3.  Data security requirements  . . . . . . . . . . . . . . .  14
     5.4.  Security Considerations . . . . . . . . . . . . . . . . .  14
   6.  Acknowledgements  . . . . . . . . . . . . . . . . . . . . . .  14
   7.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .  14
   8.  References  . . . . . . . . . . . . . . . . . . . . . . . . .  15
     8.1.  Normative References  . . . . . . . . . . . . . . . . . .  15
     8.2.  Informative References  . . . . . . . . . . . . . . . . .  15
   Appendix A.  Additional Stuff . . . . . . . . . . . . . . . . . .  15
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  15

1.  Introduction

   5G mobile communication develops rapidly driven by different
   application scenarios and diversified service deployment.  Industrial
   Internet based on 5G technology has also accelerated research and
   deployment.  To give full play to the role of 5G technology in the
   industrial system, the first prerequisite is to realize the
   interconnection between the industrial site and the cloud.  On the
   one hand, 5G spectrum access is limited, on the other hand, due to
   the constraints of industrial equipment computing resources, we

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   typically offload a portion of our industrial applications so that we
   have enough computing resources on our computing system to execute,
   such as micro server, cloud server, or data center.  Therefore, the
   multi-priority scheduling technology of industrial field data to the
   cloud is an important key to be solved.

   In the industrial field, industrial field data mainly refers to the
   real-time data collected by industrial production equipment and
   target products under the operation mode of the Internet of Things,
   including the data reflecting the operation state of equipment and
   products, such as operation and operation condition, working
   condition and environmental parameters.  These data can be uploaded
   to the cloud for data processing and analysis through 5G base
   station, and then the data can be reused by users for intelligent
   design, intelligent production, networked collaborative
   manufacturing, intelligent service and personalized customization.

   In the future smart factories, there will be a huge demand for
   industrial field applications, including Internet of Things data
   acquisition, intelligent robots, industrial AR and other services.It
   is precisely because many applications have different demands for
   network service quality that it is necessary to study the multi-
   priority scheduling technology based on service quality for the data
   of these different demands.

   From the industrial site to the cloud, 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 algorithm 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 reason, we propose a collaborative
   scheduling algorithm for big data tasks in industrial field based on
   edge computing.

1.1.  Application requirements of industrial Internet

1.1.1.  Internet of Things data collection

   The Internet of Things, is the Internet of Everything.  Internet of
   Things data contains information needed by sound, light, heat,
   electricity, mechanics, chemistry, biology and location, etc.  Its
   goal is to combine all kinds of information sensing devices with the
   Internet to realize the interconnection of people, machines and
   things at any time, any place.  Therefore, massive machine
   communication brings great demand for network coverage.  The

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   information collection of industrial control system includes the
   sensor data designed in the industrial system and mainly collects the
   physical events and data occurring in industrial production and
   manufacturing factories, including various physical quantities,
   identification, positioning and other data.

1.1.2.  Intelligent robot

   Machine vision has become more and more popular in manufacturing
   enterprises, such as automobile factories, through machine vision to
   detect product defects.  This kind of application requires a lot of
   network bandwidth.  And since intelligent robots need to complete
   corresponding intelligent operations, the fast response of highly
   reliable 5G network is also a prerequisite.

1.1.3.  Industrial AR

   5G AR will become an important application of industrial Internet.
   When combined, the two can be applied to multiple scenes in the
   industrial field, including: man-machine collaboration, monitoring of
   production process, pre-job training for new employees, product
   quality detection, remote assistance and guidance, etc.  For example,
   when industrial equipment damage alerts the network for maintenance,
   remote technicians can remotely guide on-site technicians using head-
   mounted AR equipment through the industrial AR application to
   complete the equipment maintenance process.  This makes the
   industrial network need to provide reliable network bandwidth
   guarantee and delay demand.

1.1.4.  Other businesses

   In addition, there are other business needs in the industrial field,
   including security monitoring and other businesses.  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 algorithm based on 5G slice

   In the access stage from industrial field to 5G NR, due to the
   service demand and communication quality of different data on site,
   the base station side needs to carry out multi-priority uplink
   scheduling for different service data and allocate spectrum
   resources.

   In this paper, inter-slice scheduling and intra-slice user scheduling
   are used for uplink resource allocation.  On the basis of satisfying
   the service quality required by 5G different slice scenarios and
   different industrial field data, the fairness and throughput of the
   scheduling algorithm are improved.

3.1.  Common Upline scheduling of 5G NR:

   Firstly, when UE needs to send uplink data, it will first put the
   required data into the cache, and then submit its BSR (Buffer State
   Report) to the base station through PUCCH (Physical Uplink Control
   Channel.  At the same time, the Scheduling Request is sent to inform
   the base station gNB that it needs to send data.

   Secondly, the uplink scheduler of gNB receives an uplink scheduling
   request from UE, which allocates resources to UE based on the UE's
   cache status report and the uplink channel condition of UE, which is
   obtained by the SRS Reference Signal that UE periodically sends to
   the gNB.  Distribution results are sent to UE by PDCCH (Physical
   Downlink Control Channel) using UL Grant (Uplink Grant).  Thirdly, UE
   sends data to the base station through PUSCH (Physical Uplink Shared
   Channel) using the resources allocated by the base station.

   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.

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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, which can maximize the system throughput but cannot
   guarantee fairness between cell users.  PF algorithm considers the
   ratio of instantaneous rate and long-term average rate when selecting
   users, and adjusts different users by using weight value to achieve
   the purpose of giving consideration to the overall throughput of the
   system and fairness of users at the same time, but does not consider
   QoS information of service.

   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, faced
   with different industrial scenarios, different QoS requirements of
   the business, A more reasonable multi-priority scheduling algorithm
   needs to be designed.  Under the condition of limited wireless
   resources, the algorithm makes reasonable allocation of 5G wireless
   resources in different segments, so as to meet the service
   requirements of high-priority services of intelligent factories as
   far as possible and improve the resource utilization rate and
   fairness among users.

   This section provides a multi-priority resource scheduling method for
   industrial field data to improve resource utilization as much as
   possible on the basis of meeting the service quality required by
   different industrial field services under different slice scenarios
   of 5G.

3.2.  Uplink scheduling algorithm flow

   The general flow of the uplink scheduling algorithm based on 5G slice
   is as follows:

   Step 1: During a scheduling cycle, determine if the task cache queue
   is empty.  If null, wait for the next scheduling cycle; if non-null,
   proceed to the next step.

   Step 2: Use the inter-slice scheduling algorithm to allocate
   resources to the three network slices according to requirements.

   Step 3: For the resources obtained from each slice, resource
   scheduling is carried out for each user in the slice.  When complete,
   wait for the next scheduling cycle.

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3.3.  Inter-slice scheduling

   For Step 2, inter-slice resource scheduling needs to meet:

   1.  The resources obtained from different slices are isolated and
   independent in the frequency domain, and can be adjusted flexibly.
   The congestion of one network slice does not affect other network
   slices.

   2.  By allocating spectrum resources with better channel conditions
   to the high-priority slice, the throughput of the system and the
   service guarantee for high-priority service can be improved.

   Assuming that the number of RB (Resource blocks) that the scheduler
   can configure is , and at the time of scheduling, the total number of
   users requesting resources is , the priority of each slice in each RB
   is defined in this paper by 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
   block in one scheduling cycle.  The greater P(i,j) indicates that the
   scheduling priority of i-th user in j-th RB block is higher.  In
   order to improve system throughput, the priority is based on the rate
   and calculation of all users in the j-th RB block in the i-th slice.
   R(i,j) represents the rate sum of all users in the j-th RB block in
   the i-th slice According to the calculation, we can get the priority
   matrix.

   For the service requirements of different businesses of industrial
   field data, the uRLLC slice needs low delay and high reliability,
   such as a remote real-time cooperative robot, which needs to give
   priority to allocation of resources and reduce queuing delay.  The
   eMMB slice will have a lot of high data volume business requirements,
   priority allocation of resources will improve the overall network
   throughput, but doesn't have such as high requirements for the delay
   as 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, according to the order
   of the uRLLC slice, the eMMB slice, and the mMTC slice, RB resources
   are configured according to the priority matrix.

   According to the resource scheduling requirements of slices, the
   final inter-slice resource scheduling scheme is as follows:

   Step 1: Calculate the priority matrix according to formula (1);

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   Step 2: According to the priority order of slices, select the i-th
   slice for resource scheduling, and i is initialized to 1;

   Step 3: Select the i-th slice, the j-th RB block with priority
   ranking, and j is initialized to 1;

   Step 4: Determine whether the currently scheduled RB block is
   adjacent to the RB that the slice has been allocated.  If yes,
   perform step 5.  If no, set j=j+1 and repeat step 3.

   Step 5: Assign priority j-th RB to the i-th slice and remove that RB
   from the RB queue;

   Step 6: Determine whether the resource request for the i-th slice has
   obtained enough resources.  If so, perform step 7.  If not, set j=j+1
   and re-perform step 3.

   Step 7: Determine whether the RB sequence is empty or whether all
   slices have obtained sufficient resources; if so, end slice resource
   scheduling; if not, execute i = i +1 and repeat step 2.

3.4.  Intra-slice user scheduling

   After resource scheduling among slices, these slices obtaine their
   respective continuous and isolated RB groups, and then the intra-
   slice user scheduling will not interfere with each other.

   The user scheduling in the slice can be understood as a logical cell,
   and the scheduler will conduct resource scheduling on the users
   belonging to this cell through the resources obtained by intra-slice
   scheduling.  Because of different QoS requirements of data in 5G
   industrial field, 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 i-th user in the j-th RB group at time t is
   calculated by the formula (2) :

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

   p(i) represents the maximum rate of packet loss of user i, p(i) in
   (0,1).  Therefore, -log(p(i)) indicates the lower the maximum loss
   rate, the higher the priority.  Td(i) represents the maximum wait
   delay for the user i, The smaller Td(i)the higher the user priority.
   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

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   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
   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 t is, and the higher the priority is.

   Therefore, the intra-slice user scheduling scheme is as follows:

   Step 1: Complete the inter-slice scheduling.

   Step 2: For all RB of a single slice, it is divided into the same
   size RB group 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 the user priority.

   Step 5: Determine whether the RB sequence is empty, if so, the
   resource allocation ends, if not, repeat step 4.

3.5.  The summary of this chapter

   The algorithm proposed in this section has the following advantages:

   1.  Analyze the different service requirements of different services
   for industrial field data in 5G environment.  The inter-slice
   scheduling algorithm is used to complete the resource allocation of
   5G three slices to ensure the flexible scheduling and isolation of
   resources between slices.  Ensure 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 intra-slice scheduling comprehensively considers the data
   service transmission rate, delay demand, packet loss rate, data
   volume to be transmitted, and other performance indicators, and 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.  Collaborative scheduling algorithm based on edge computing for big
    data task in 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 other applications having higher requirements for
   computing, Relying only on the current centralized cloud computing
   architecture model, The computing power to provide business is not
   enough.  Facing the continuous generation of big data in industrial
   production, entertainment, education, and other industries, there is
   an urgent need for cloud-centric computing architecture to expand to
   a distributed computing service architecture, and the most
   representative of these are edge computing and fog computing model.

   With the emergence of big data, the computing power of mobile
   terminals has also begun to rise.  According to the type of data and
   service quality requirements, higher requirements are put forward for
   computing speed and processing capacity.  Some tasks with
   particularly high latency requirements are suitable for distributed
   processing mechanisms, relying on cloud processing, and real-time
   performance cannot be met under conditions of heavy network load.
   Therefore, it is necessary to rely on edge computing to sink
   computing power, and dynamically allocate computing resources based
   on tasks and real-time performance.  In the industrial field
   environment, there are various sensor data, and the corresponding
   instruction requirements generated by them, and service processing.
   To a certain extent, the computing power of field devices is
   insufficient, and the amount of data presents a complex and huge
   trend.  It is difficult to only rely on edge terminal equipment to
   implement business logic.  At the same time, in complex industrial
   sites, the response requirements of various services are
   inconsistent, so the system's response capabilities, processing
   capabilities, and throughput capabilities have a huge test.

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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 have a huge research space and also have
   important practical value, attracting the attention of many scholars.
   However, the traditional research point is to think about the
   resource allocation and utilization of edge nodes, as well as the
   coordinated scheduling of the edge and the cloud, or to focus on the
   issue of task priority.  However, these algorithms have drawbacks,
   either considering the resource allocation problem between nodes, or
   only considering the task priority problem, and not coordinated
   consideration, at the same time, the network link bandwidth also has
   an impact on the system.  Therefore, it is necessary to propose a
   better task scheduling algorithm based on task requirements, combined
   with edge computing, network bandwidth, and task real-time
   requirements, to maximize resource utilization and user satisfaction.

4.1.  Task collaborative scheduling algorithm flow

   Based on the complex industrial site environment, task requirements
   and the different characteristics of the amount of calculation, from
   a new perspective, comprehensive consideration of computing
   resources, user satisfaction, through edge computing technology to
   achieve business tasks between the terminal and edge server resource
   scheduling problem, in In the case of meeting the minimum resources,
   user satisfaction can also be guaranteed to meet the real-time task
   processing generated in a variety of industrial production processes.

   Based on 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.  As shown in
   Figure 4, the method steps are: the terminal publishes the service,
   and the scheduler obtains the service information.  Such as
   calculating the number of tasks, delay requirements.  According to
   the task delay requirements, the task's urgency and priority are
   evaluated.  According to the current bandwidth resources, it is
   determined whether the task should be offloaded to the edge server.
   The task cache status of the scheduler at this time and the computing
   information of each edge server are obtained.  The number of queued
   processing is based on system resource status, current business task
   volume, delay requirements, etc. to perform reasonable task
   scheduling of the server and terminal, and repeat the execution until
   all tasks are allocated and executed accordingly.

   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 ,
   the delay requirements:T(i),with i=1 ... n.

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   Step 2: The task priority: LEVEL(i), with i=1 ... n,. is evaluated
   according to the time delay requirements of the task, which has an
   impact on the subsequent task scheduling and further reflects user
   satisfaction.

   Step 3: Obtain the current network link information, that is, the
   remaining bandwidth of the network: R.  Assuming that the upload rate
   of the task is α , the task upload needs to meet, if the current
   bandwidth is not enough, the task needs to be unloaded and the
   waiting delay is recorded.

   Step 4: Obtain the task scheduling threshold of the scheduler at this
   time, as well as the calculation information of each edge server, the
   number of tasks queued, and the queue waiting for the delay.

   Step 5: Perform reasonable task scheduling of the server and terminal
   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:
   according to the machine learning algorithm, the task delay
   requirements are comprehensively considered, 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.  The task
   is assigned to the edge server to calculate the total delay, the
   output layer is maximized through the fully connected layer, and
   finally, 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 it can be based on system
   resources conditions, network load and adaptive adjustment parameters
   provide a basis for subsequent optimization.  The number of
   iterations can be determined by yourself.  Finally, the updated
   parameters are used to allocate real resources to the tasks in the
   current scheduler.

4.3.  The summary of this chapter

   Compared with the existing industrial field task resource
   collaborative scheduling algorithm, the algorithm proposed in this
   section has the following advantages:

   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 Reduce the resource consumption of the system and avoid
   the calculation waiting for behavior caused by the unbalanced

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   resource allocation; consider the execution status of the system, the
   task calculation amount, and the delay requirements for optimal
   scheduling when performing task scheduling; comprehensively consider
   the construction of new optimization goals, In order to achieve
   system resource utilization efficiency and user satisfaction.

5.  Security Considerations

5.1.  Security Considerations

   For edge computing equipment, security problems caused by indirect or
   self-inflicted causes during operation (e.g. energy supply;Cooling
   and dust removal, equipment loss, etc.), although the operation
   threat is not as complete as the damage caused by natural disasters,
   the lack of a good response means will still lead to disastrous
   consequences, resulting in the performance degradation of edge
   computing, service interruption and data loss.Especially in the
   industrial Internet scene, the factory is more professional in the
   maintenance and overhaul of its own equipment, but IT is difficult to
   timely deal with the operation and maintenance of IT equipment.

5.2.  Network security requirements

   Compared with cloud computing data centers, edge nodes have limited
   capabilities and are more vulnerable to hackers.  Although the damage
   of a single damaged edge node is not great, and the network has the
   ability to quickly find nearby alternative nodes; But if hackers use
   the compromised edge nodes as "broilers" to attack other servers, it
   could affect the entire network.  Most of the existing security
   protection technologies have complex computational protection
   processes, which are not suitable for edge computing scenarios.
   Therefore, it is a great demand for network security to design
   lightweight security technology suitable for edge computing
   architecture in industrial Internet scene.

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5.3.  Data security requirements

   In edge computing, users outsource data to edge nodes and transfer
   control of data to edge nodes, which introduces the same security
   threats as cloud computing.  First, it is difficult to ensure the
   confidentiality and integrity of the data because the outsourced data
   may be lost or modified incorrectly.  Second, unauthorized parties
   may misuse the uploaded data to seek other benefits.  Compared with
   the cloud, edge computing has avoided the long-distance transmission
   of multiple routes and greatly reduced 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, how to realize the safe and rapid migration
   of data after the collapse of an edge node.

5.4.  Security Considerations

   Application security, as the name implies, guarantees the security of
   the application process and results.  In the era of marginal big data
   processing, by moving more and more application services from cloud
   computing centers to network edge nodes, applications can be
   guaranteed to get shorter response time and higher reliability, and
   meanwhile, network transmission bandwidth and intelligent terminal
   power consumption can be greatly saved.  However, edge computing not
   only has common application security problems in information systems,
   such as denial of service attack, unauthorized access, software
   vulnerability, abuse of authority, identity impersonation, etc., but
   also has other application security requirements due to its own
   characteristics.  In the scenario where multiple security domains and
   access networks coexist at the edge, how to manage user identity and
   realize authorized access to resources become very important to
   ensure application security.

6.  Acknowledgements

   This template was derived from an initial version written by Pekka
   Savola and contributed by him to the xml2rfc project.

   This document is part of a plan to make xml2rfc indispensable
   [DOMINATION] .

7.  IANA Considerations

   This memo includes no request to IANA.

   All drafts are required to have an IANA considerations section (see
   Guidelines for Writing an IANA Considerations Section in RFCs
   [RFC5226] for a guide).  If the draft does not require IANA to do

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   anything, the section contains an explicit statement that this is the
   case (as above).  If there are no requirements for IANA, the section
   will be removed during conversion into an RFC by the RFC Editor.

8.  References

8.1.  Normative References

   [min_ref]  authSurName, authInitials., "Minimal Reference", 2006.

   [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

   [DOMINATION]
              Mad Dominators, Inc., "Ultimate Plan for Taking Over the
              World", 1984, <http://www.example.com/dominator.html>.

   [RFC2629]  Rose, M., "Writing I-Ds and RFCs using XML", RFC 2629,
              DOI 10.17487/RFC2629, June 1999,
              <https://www.rfc-editor.org/info/rfc2629>.

   [RFC3552]  Rescorla, E. and B. Korver, "Guidelines for Writing RFC
              Text on Security Considerations", BCP 72, RFC 3552,
              DOI 10.17487/RFC3552, July 2003,
              <https://www.rfc-editor.org/info/rfc3552>.

   [RFC5226]  Narten, T. and H. Alvestrand, "Guidelines for Writing an
              IANA Considerations Section in RFCs", RFC 5226,
              DOI 10.17487/RFC5226, May 2008,
              <https://www.rfc-editor.org/info/rfc5226>.

Appendix A.  Additional Stuff

   This becomes an Appendix.

Authors' Addresses

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

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   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

   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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