TWI778597B - Deployment method of remote desktop gateways, computer device, and storage medium - Google Patents

Abstract

The present application provides a deployment method of remote desktop gateways, a computer device, and a storage medium. The deployment method of remote desktop gateways includes: obtaining application data of virtual machines(VMs) in the remote desktop gateways(RDGWs), the application data including historical data and real-time data; training a neural network with the historical data to obtain a target classification model; inputting the real-time data into the target classification model to obtain a classification result of the real-time data; deploying the VMs to a designated RDGW based on the classification result; and adding RDGW or recycling existing RDGWs based on the real-time data. The application can assist in the elastic scaling and deployment of resources and save resources while meeting resource requirements.

Description

Deployment method, computer device and storage medium of remote desktop gateway

The present invention relates to the field of computer technology, and in particular, to a method for configuring a remote desktop gateway, a computer device and a storage medium.

In the process of using the virtualization technology, the problem of insufficient or excessive resources of the remote desktop gateway usually occurs. How to ensure that there is an appropriate amount of remote desktop gateway resources at all times to handle current traffic demands, and how to instantly discover and solve the waste of remote desktop gateway resources are problems faced by the computer technology field.

In view of the above content, it is necessary to provide a remote desktop gateway allocation method, computer device and storage medium, which can realize elastic scaling and allocation of resources and save resources while meeting resource requirements.

The method for deploying the remote desktop gateway includes: acquiring application data of a virtual machine in the remote desktop gateway, where the application data includes historical data and real-time data; using the historical data to train a neural network to obtain a target classification model; Input the real-time data into the target classification model to obtain a classification result of the real-time data; based on the classification result, allocate the virtual machine to a designated remote desktop gateway; and based on the real-time data, add a new Remote desktop gateway or recycle existing remote desktop gateway.

Optionally, the method further includes: monitoring the virtual machine, obtaining the application information and storing the application data in a preset database.

Optionally, the historical data includes: the identification number ID of the user logging in to the virtual machine, the time b1 when the user logs in to the virtual machine, and the time e1 when the user logs out of the virtual machine; the real-time data includes all The usage state of the virtual machine, the usage state of the virtual machine includes an unused state, a logged-in state, and a state in which a login operation is being performed; when the virtual machine's usage state is the logged-in state Or when the login operation is being performed, the real-time data further includes the ID of the user who performs the login operation on the virtual machine, and the time b2 when the user performs the login operation on the virtual machine.

Optionally, the method further includes: acquiring the historical data from the database as a training sample, training the neural network to obtain a classification model; and performing at least one iteration of updating the classification model until all The classification model meets the preset requirements, and the target classification model is obtained.

Optionally, performing at least one iterative operation update on the classification model includes: judging whether the classification model after the last iterative operation update meets the preset requirements; when the classification model after the last iterative operation update meets the preset requirements; When meeting the preset requirements, the classification model updated by the last repeated operation is used as the target classification model; when the classification model updated by the last repeated operation does not meet the preset requirements, Obtain the updated historical data from the database as a training sample, continue to train the neural network on the basis of the updated classification model after the last repeated operation, and obtain the currently updated classification model. The updated classification model is updated for the next iteration.

Optionally, inputting the real-time data into the target classification model, and obtaining a classification result of the real-time data includes: when the real-time data indicates that the state of the virtual machine is a state in which a login operation is being performed. , the ID of the user who will perform the login operation on the virtual machine and the time b2 when the user performs the login operation on the virtual machine are input into the target classification model; using the target classification model, it is predicted that the user logs out of the virtual machine The time e2 of the virtual machine; comparing the time e2 with the range of each time period in the preset number of time periods, it will be in the The IDs of the users corresponding to the e2 in the same time period are classified into one category, and the preset number of classification results are obtained in total.

Optionally, the allocating the virtual machine to the specified remote desktop gateway based on the classification result includes: allocating the virtual machine whose ID of the user belonging to the same class is performing the login operation to the virtual machine. The specified remote desktop gateway.

Optionally, the adding a remote desktop gateway or recycling an existing remote desktop gateway based on the real-time data includes: analyzing the real-time data; when determining the storage of the existing remote desktop gateway. When the average usage exceeds the preset threshold, a new alert is issued and one or more remote desktop gateways are added; or when it is determined that the usage status of all virtual machines in any existing remote desktop gateway is When not in use, a recycling warning is issued and any existing remote desktop gateway is recycled.

The computer-readable storage medium stores at least one instruction, and when the at least one instruction is executed by the processor, implements the method for provisioning the remote desktop gateway.

The computer device includes a memory and at least one processor, the memory stores at least one instruction, and when the at least one instruction is executed by the at least one processor, implements the method for provisioning the remote desktop gateway.

Compared with the prior art, the deployment method of the remote desktop gateway, the computer device and the storage medium can realize elastic scaling and deployment of resources, and save resources while meeting resource requirements.

3: Computer device

32: Processor

31: Storage

301: Monitoring System

302: Allocation system

303: Alarm system

S1~S4: Steps

In order to more clearly illustrate the technical solutions in the embodiments of the present application or in the prior art, the following briefly introduces the accompanying drawings that need to be used in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only It is an embodiment of the present application. For those of ordinary skill in the art, other drawings can also be obtained according to the provided drawings without any creative effort.

FIG. 1 is an application environment diagram of a method for configuring a remote desktop gateway according to a preferred embodiment of the present application.

FIG. 2 is a flowchart of a method for configuring a remote desktop gateway according to a preferred embodiment of the present application.

FIG. 3 is an application schematic diagram of a method for deploying a remote desktop gateway according to a preferred embodiment of the present application.

FIG. 4 is a structural diagram of a computer device according to a preferred embodiment of the present application.

In order to more clearly understand the above objects, features and advantages of the present application, the present application will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments may be combined with each other in the case of no conflict.

In the following description, many specific details are set forth to facilitate a full understanding of the present application, and the described embodiments are only a part of the embodiments of the present application, but not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the technical field to which this application belongs. The terms used herein in the specification of the application are for the purpose of describing specific embodiments only, and are not intended to limit the application.

FIG. 1 is an application environment diagram of a method for configuring a remote desktop gateway according to a preferred embodiment of the present application. As shown in FIG. 1 , it is a connection relationship between a virtual machine (Virtual Machine, VM) and a remote desktop gateway (Remote Desktop Gateway, RDGW). The following will describe in detail with reference to the method flowchart shown in FIG. 2 .

Referring to FIG. 2 , it is a flowchart of a method for configuring a remote desktop gateway according to a preferred embodiment of the present application.

In this embodiment, the method for deploying the remote desktop gateway can be applied to a computer device (for example, the computer device 3 shown in FIG. 4 ). The function for provisioning the remote desktop gateway provided by the method of the present application is integrated on the computer device, or runs on the computer device in the form of a software development kit (Software Development Kit, SDK).

As shown in FIG. 2 , the method for configuring the remote desktop gateway specifically includes the following steps. According to different requirements, the order of the steps in the flowchart can be changed, and some steps can be omitted.

Step S1, the monitoring system 301 acquires application data of the virtual machine in the remote desktop gateway, and the application data includes historical data and real-time data.

In one embodiment, each remote desktop gateway has its own number, and different RDGWs have different numbers, for example, RDGW1, RDGW2, etc.; each of the virtual machines has its own number, and different RDGWs have different numbers. VMs have different numbers, eg VM1, VM2, etc.

In one embodiment, in response to a user's login operation, the VM is used by the logged-in user through the RDGW. For example, as shown in FIG. 1 , one VM is allocated to one RDGW, and one RDGW may contain no more than a preset number of (eg, 50) of VMs.

In one embodiment, each user has its own identity document (ID), and different users have different IDs, for example, user A, user B, and so on.

In one embodiment, as shown in FIG. 3 , the monitoring system 301 monitors the VM, obtains the application data, and stores the application data in a preset database. The historical data includes: the ID of the user logging in to the VM, the time b1 when the user logs in to the VM, and the time e1 when the user logs out of the VM; the real-time data includes the usage status of the VM, the usage of the VM The status includes an unused status, a logged-in status, and a status in the process of performing a login operation. The unused status includes a status of not performing a login operation and an The status in which the login operation is performed and has been successfully logged in (authentication is passed, for example, the ID and password entered by the user are determined to be correct) and used, and the status in the login operation being performed includes that the user is performing the login operation (for example, the Enter ID) and not yet The status of successful login (for example, the password has not been entered); when the usage status of the VM is the logged-in status or the status of the login operation being performed, the real-time data also includes a The ID of the user who performed the login operation of the VM, and the time b2 when the user performed the login operation to the VM.

In one embodiment, the historical data includes: data on the user's use of the VM during working hours on historical working days. It should be noted that the data in historical non-working days (for example, weekends, holidays, etc.) are not recorded in the historical data because they are not representative.

Step S2, the computer device uses the historical data to train a neural network to obtain a target classification model.

In one embodiment, as shown in FIG. 3 , the computer device obtains the historical data from the database as a training sample, trains the neural network to obtain a classification model; and performs at least one repeated operation on the classification model Update until the classification model meets the preset requirements, and obtain the target classification model. The performing at least one iterative operation update on the classification model includes: judging whether the classification model updated by the last iterative operation meets the preset requirements; when the classification model updated by the last repeated operation meets the preset requirements; When setting the requirements, the classification model updated by the last repeated operation is used as the target classification model; when the classification model updated by the last repeated operation does not meet the preset requirements, from the data Obtain the updated historical data in the library as a training sample, continue to train the neural network on the basis of the updated classification model after the last repeated operation, obtain the currently updated classification model, and analyze the currently updated classification model. Do the next iterative update.

In one embodiment, the preset requirement includes a preset number of time periods, each of the preset number of time periods may include a preset time length, and the preset number may include : The number of working hours divided according to the preset time length. For example, the working time from 9:00 to 18:00 is divided into a time period every 20 minutes to obtain 27 time periods, for example, 17:00 to 17:20 and so on.

In one embodiment, it is judged that the classification model updated by the last repeated operation is Whether or not it meets the preset requirements includes: inputting the ID of any user in the historical data to log in to the VM and the time b10 for logging in to the VM, and inputting the classification model after the last repeated operation and updating; obtaining the any of the The predicted time e20 when the user logs out of the VM; compare the predicted time e20 with the time e10 of any user in the historical data to log out of the VM, and determine the predicted time e20 and the time Whether e10 is within the range of the same time period in the preset number of time periods, a judgment result is obtained; when the judgment result is yes, it is determined that the classification model updated by the last repeated operation conforms to the preset requirements; when the judgment result is no, it is determined that the classification model updated by the last repeated operation does not meet the preset requirements.

Step S3, the monitoring system 301 inputs the real-time data into the target classification model, and obtains a classification result of the real-time data.

In one embodiment, as shown in FIG. 3 , the monitoring system 301 inputs the real-time data in the database into the target classification model, when the real-time data indicates that the status of the VM is being logged in In the state of , the computer device will input the ID of the user who performs the login operation on the VM and the time b2 when the user performs the login operation on the VM into the target classification model; the target classification model predicts that the user logs out The time e2 of the VM; the target classification model compares the time e2 with the range of each time period in a preset number of time periods, and classifies the IDs of the users corresponding to the e2 in the same time period. For one category, the preset number of classification results are obtained. For example, user A logs in to VM1 through RDGW1 at 13:16, and the target classification model predicts that user A's logout time is 17:05; at 14:27, user C logs in to VM2 through RDGW2, The target classification model predicts that the logout time of user C is 17:15; the target classification model determines that both user A and user C will log out within the time period from 17:00 to 17:20, and classify user A and user C into one category .

Step S4, the target classification model allocates the virtual machine to a designated remote desktop gateway based on the classification result; and the allocation system 302 adds a new remote desktop gateway or recycles an existing remote desktop based on the real-time data gateway.

In one embodiment, such as shown in Figure 3, the target classification model will belong to the same class of The VM whose user ID is performing the login operation is allocated to the specified RDGW. The designated RDGW may be an RDGW logged in and used by the user's ID belonging to the same class. For example, at 14:27, user A is logging in through RDGW1 and using VM1, and user C performs a login operation to VM2 through RDGW2. The target classification model determines that user C and user A belong to the same category, and assigns the VM2 that user C is logging in to perform operations on. RDGW1 is given to user A to log in and use.

In one embodiment, such as shown in FIG. 3 , the alarm system 303 analyzes the real-time data, and the analysis includes: judging whether the average storage usage of the existing RDGW exceeds a preset threshold (for example, 70%) For example, to determine whether the average usage rate of the storage of the existing RDGWs within a preset evaluation period (for example, 3 minutes) is greater than 70%, the existing RDGWs are RDGW1 and RDGW2, and the average storage capacity of RDGW1 within 3 minutes is The usage rate is 80%, and the average usage rate of RDGW2 within 3 minutes is 70%, then the average usage rate of existing RDGWs exceeds the preset threshold of 70%; and it is determined whether there is an existing RDGW The usage states of all VMs in the RDGW are in the unused state; when it is determined that the average usage rate of the storage of the existing RDGW exceeds the preset threshold, a new alert is issued to the deployment system 302, and the deployment system 302 adds a new or multiple RDGWs; and when it is determined that the usage status of all VMs in any existing remote desktop gateway is in an unused state, a recycling alert is issued to the provisioning system 302, and the provisioning system 302 responds to the any existing remote desktop gateways. RDGM is recycled. For example, when it is determined that the average storage usage of the existing RDGWs exceeds 70%, the computer device will issue a new alert and add a new RDGW; at 17:20, all users in RDGW1 have logged out of the VM , the computer device recycles RDGW1.

2-3 above describe the deployment method of the remote desktop gateway of the present application in detail, and the following describes the hardware device architecture for implementing the deployment method of the remote desktop gateway with reference to FIG. 4 .

It should be understood that the embodiments are only used for illustration, and are not limited by this structure in the scope of the patent application.

Referring to FIG. 4, it is a schematic structural diagram of a computer device according to a preferred embodiment of the present application picture. In a preferred embodiment of the present application, the computer device 3 includes a storage 31 and at least one processor 32 . Those skilled in the art should understand that the structure of the computer device shown in FIG. 4 does not constitute a limitation of the embodiments of the present application, and may be either a busbar type structure or a star structure. More or less other hardware or software, or different component arrangements are shown.

In some embodiments, the computer device 3 includes a terminal capable of automatically performing numerical calculations and/or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, dedicated integrated circuits, Programmable gate arrays, digital signal processors and embedded devices, etc.

It should be noted that the computer device 3 is only an example, and other existing or future electronic products, if applicable to the present application, should also be included within the protection scope of the present application, and are incorporated herein by reference.

In some embodiments, the storage 31 is used to store code and various data. For example, the storage 31 can be used to store the preset database, and can also store the monitoring system 301 , the deployment system 302 and the alarm system 303 installed in the computer device 3 , and the computer device 3 is running In the process, the program or data access can be completed at high speed and automatically. The storage 31 includes a Read-Only Memory (ROM), a Programmable Read-Only Memory (PROM), and an Erasable Programmable Read-Only Memory (Erasable Programmable Read). -Only Memory, EPROM), One-time Programmable Read-Only Memory (OTPROM), Electronically-Erasable Programmable Read-Only Memory (EEPROM) , Compact Disc Read-Only Memory (CD-ROM) or other optical disk storage, magnetic disk storage, magnetic tape storage, or any other computer-readable storage medium that can be used to carry or store data.

In some embodiments, the at least one processor 32 may be composed of an integrated circuit, for example, may be composed of a single packaged integrated circuit, or may be composed of a plurality of the same function or different It is composed of functionally packaged integrated circuits, including one or more central processing units (CPUs), microprocessors, digital signal processing chips, graphics processors, and combinations of various control chips. The at least one processor 32 is the control core (Control Unit) of the computer device 3, and uses various interfaces and lines to connect the various components of the entire computer device 3, by running or executing the program stored in the storage 31 or module, and call the data stored in the storage 31 to execute various functions of the computer device 3 and process data, such as the function of configuring the remote desktop gateway.

In some embodiments, the monitoring system 301 runs in the computer device 3 . The monitoring system 301 may include a plurality of functional modules composed of program code segments. The code of each program segment in the monitoring system 301 can be stored in the storage 31 of the computer device 3 and executed by at least one processor 32 to realize the acquisition of virtual data in the remote desktop gateway RDGW in step S1. The function of the application profile of the machine VM.

In some embodiments, the dispensing system 302 runs on the computer device 3 . The deployment system 302 may include a plurality of functional modules composed of code segments. The code of each program segment in the deployment system 302 can be stored in the memory 31 of the computer device 3 and executed by at least one processor 32 to realize the addition of RDGM in step S4 or the recycling of the RDGW. Function.

In some embodiments, the alarm system 303 runs in the computer device 3 . The alarm system 303 may include a plurality of functional modules composed of program code segments. The code of each program segment in the alarm system 303 can be stored in the memory 31 of the computer device 3 and executed by at least one processor 32, so as to realize the process of issuing a new alert and issuing a recycling alert in step S4. Function. In this embodiment, the monitoring system 301 , the deployment system 302 or the alarm system 303 can be divided into a plurality of functional modules respectively according to the functions performed by them. The module referred to in this application refers to a series of computer program segments that can be executed by at least one processor and can perform fixed functions, and are stored in a memory.

Although not shown, the computer device 3 may also include a power supply for powering the various components (For example, a battery), preferably, a power source can be logically connected to the at least one processor 32 through a power management device, so that functions such as charging, discharging, and power consumption management can be implemented through the power management device. The power supply may also include one or more of a DC or AC power source, a recharging device, a power failure detection circuit, a power converter or inverter, a power supply status indicator, or any other element. The computer device 3 may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

It should be understood that the embodiments are only used for illustration, and are not limited by this structure in the scope of the patent application.

The above-mentioned integrated units implemented in the form of software function modules can be stored in a computer-readable storage medium. The above-mentioned software function module is stored in a storage medium, and includes several instructions for causing a computer device (which may be a server, a personal computer, etc.) or a processor (processor) to execute parts of the methods described in the various embodiments of the present application.

In a further embodiment, referring to FIG. 4 , the at least one processor 32 can execute the operating device of the computer device 3 and various types of installed applications (such as the monitoring system 301 , the deployment system 302 and all the installed applications). The alarm system 303), program codes, etc., for example, the above-mentioned modules.

The storage 31 stores program codes, and the at least one processor 32 can call the program codes stored in the storage 31 to execute related functions. The program codes stored in the storage 31 can be executed by the at least one processor 32, so as to realize the functions of the various modules to achieve the purpose of deployment of the remote desktop gateway.

In one embodiment of the present application, the storage 31 stores one or more instructions (ie, at least one instruction), and the at least one instruction is executed by the at least one processor 32 to implement the remote control shown in FIG. 2 . The purpose of the deployment of the terminal desktop gateway.

In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the device embodiments described above are only illustrative. For example, the division of the modules is only a logical function division, and the actual implementation There may be other divisions.

The modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical units, that is, they can be located in one place or distributed to multiple networks. on the unit. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution in this embodiment.

In addition, each functional module in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit. The above-mentioned integrated units can be implemented in the form of hardware, or can be implemented in the form of hardware plus software function modules.

It will be apparent to those skilled in the art that the present application is not limited to the details of the above-described exemplary embodiments, but that the present application can be implemented in other specific forms without departing from the spirit or essential characteristics of the present application. Accordingly, the embodiments are to be regarded in all respects as illustrative and not restrictive, and the scope of this application is defined by the appended claims rather than the foregoing description, and is therefore intended to fall within the scope of the claims. All changes within the meaning and scope of the equivalents of , are included in this application. Any reference sign in a claim should not be construed as limiting the claim to which it relates. Furthermore, it is clear that the word "comprising" does not exclude other units or, and the singular does not exclude the plural. Multiple units or means stated in the device claim may also be implemented by one unit or means through software or hardware. The terms first, second, etc. are used to denote names and do not denote any particular order.

Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application rather than limitations. Although the present application has been described in detail with reference to the above preferred embodiments, those of ordinary skill in the art should The technical solutions can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present application.

S1~S4: Steps

Claims (6)

A method for deploying a remote desktop gateway, applied to a computer device, wherein the method comprises: obtaining the remote desktop from the remote desktop gateway according to the serial number of the remote desktop gateway and the serial number of the virtual machine The application data of the virtual machine in the gateway, the application data includes historical data and real-time data; use the historical data as a training sample to train a neural network to obtain a classification model, and perform at least one repeated operation update on the classification model until the The classification model meets the preset requirements, and the classification model that meets the preset requirements is determined as the target classification model, wherein the preset requirements include a preset number of time periods, among which the preset number of time periods Each time period of the time period includes a preset time length, and the preset number includes the number after dividing the working time according to the preset time length; input the real-time data into the target classification model, and obtain the The classification result of the real-time data, including: when the real-time data indicates that the state of the virtual machine is a state in which a login operation is being performed, the identification number ID of the user who will perform the login operation on the virtual machine and the user The time b2 of performing the login operation on the virtual machine is input into the target classification model; using the target classification model, predict the time e2 when the user logs out of the virtual machine; compare the time e2 with the preset number Compare the range of each time period in the time period, classify the IDs of the users corresponding to the e2 in the same time period into one category, and obtain the preset number of classification results; based on the classification results, Allocating the virtual machine to a designated remote desktop gateway includes: allocating a virtual machine whose ID of the user belonging to the same class is performing a login operation to the designated remote desktop gateway; and based on the real-time Data, adding a remote desktop gateway or recycling an existing remote desktop gateway, including: when it is determined that the average storage usage of the existing remote desktop gateway exceeds the preset threshold, a new warning is issued and a new One or more remote desktop gateways are added; or when it is determined that the use status of all virtual machines in any existing remote desktop gateway is an unused state, a recycling warning is issued and any one of the The existing remote desktop gateway is recycled. The method for provisioning a remote desktop gateway according to claim 1, wherein the method further comprises: monitoring the virtual machine to obtain the application data; and storing the application data in a preset database . The method for deploying a remote desktop gateway according to claim 1, wherein the historical data includes: the identification number ID of the user who logs in to the virtual machine, the time b1 when the user logs in to the virtual machine, and the user logs out. The time e1 of the virtual machine; the real-time data includes the use state of the virtual machine, and the use state of the virtual machine includes the unused state, the logged-in state, and the state in which the login operation is being performed; When the use state of the virtual machine is the logged-in state or the state of being logged in, the real-time data further includes the ID of the user who performs the login operation on the virtual machine, the user's The time b2 at which the virtual machine performs the login operation. The method for deploying a remote desktop gateway according to claim 2, wherein the performing at least one iterative operation update on the classification model comprises: judging whether the classification model after the last iterative operation and update conforms to the preset The requirements include: inputting the ID of any user in the historical data to log in to the VM and the time b10 of logging in to the VM, and inputting the classification model updated by the last repeated operation; obtaining the logout of the VM by the any user the predicted time e20; compare the predicted time e20 with the time e10 when any user logs out of the VM in the historical data, and determine whether the predicted time e20 and the time e10 are within the predicted time e10. Within the range of the same time period in the set number of time periods, the judgment result is obtained; when the judgment result is yes, it is determined that the classification model updated by the last repeated operation meets the preset requirements; when the When the judgment result is no, it is determined that the classification model updated by the last repeated operation does not meet the preset requirements; when the classification model updated by the last repeated operation meets the preset requirements, all The classification model after the above-mentioned iterative operation update last time is used as the target classification model; When the classification model updated by the last repeated operation does not meet the preset requirements, the updated historical data is obtained from the database as a training sample, and the classification model after the last repeated operation is updated. Continue to train the neural network on the basis of , obtain the currently updated classification model, and perform the next iterative operation update on the currently updated classification model. A computer-readable storage medium, wherein the computer-readable storage medium stores at least one instruction, and when the at least one instruction is executed by a processor, implements the remote desktop gateway as described in any one of claim items 1 to 4 allocation method. A computer device, wherein the computer device includes a memory and at least one processor, the memory stores at least one instruction, and when the at least one instruction is executed by the at least one processor, the implementation of claim 1 to The deployment method of the remote desktop gateway described in any one of 4.

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