Cloud terminal scheduling method and device, electronic equipment, storage medium and product

Abstract

The present disclosure provides a cloud terminal scheduling method and device, electronic equipment, storage medium and program product, relates to the technical field of computers, in particular to the technical field of virtual terminals. The specific implementation scheme is: according to the historical running data of the cloud applications in the cloud application set, predicting the number of users of the cloud applications in the cloud application set in a preset time period; according to the number of users, determining the number of cloud terminals running in the preset time period; running the above number of cloud terminals in advance before the preset time period arrives. According to the actual use demand of the user for the cloud terminal, the present disclosure flexibly deploys a corresponding number of cloud terminals, and improves the running flexibility of the cloud terminal and the utilization rate of the cloud terminal resources.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of computer, in particular to the technical field of virtual terminal, and especially to a cloud terminal scheduling method and device, an electronic device, a storage medium and a computer program product, which can be used in a cloud terminal scenario. BACKGROUND

[0002] A cloud phone is based on virtualization technology, and through a virtual native phone instance in the cloud, a user can remotely and in real time control the cloud phone to realize cloud running of various applications. A traditional cloud phone needs to be rented by a user in a monthly unit, but actually the user may not need to use the cloud phone for a long time, but only needs to use a certain cloud application for a short time. The fixed mode of renting a cloud phone leads to waste of cloud phone resources and user rental costs. SUMMARY

[0003] The present disclosure provides a cloud terminal scheduling method and device, an electronic device, a storage medium and a computer program product.

[0004] According to a first aspect, a cloud terminal scheduling method is provided, including: predicting, according to historical running data of cloud applications in a cloud application set, a user usage quantity of the cloud applications in the cloud application set in a preset time period; determining, according to the user usage quantity, a running quantity of cloud terminals running in the preset time period; and running the cloud terminals in advance before the preset time period arrives.

[0005] According to a second aspect, a cloud terminal scheduling device is provided, including: a first prediction unit configured to predict, according to historical running data of cloud applications in a cloud application set, a user usage quantity of the cloud applications in the cloud application set in a preset time period; a first determination unit configured to determine, according to the user usage quantity, a running quantity of cloud terminals running in the preset time period; and a running unit configured to run the cloud terminals in advance before the preset time period arrives.

[0006] According to a third aspect, an electronic device is provided, including: at least one processor; and a memory communicatively connected with the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method described in any implementation manner of the first aspect.

[0007] According to a fourth aspect, a non-transitory computer-readable storage medium storing computer instructions is provided, and the computer instructions are used to enable a computer to perform the method described in any implementation manner of the first aspect.

[0008] According to a fifth aspect, there is provided a computer program product comprising a computer program which, when executed by a processor, implements the method as described in any implementation of the first aspect.

[0009] According to the technology of the present disclosure, a scheduling method of a cloud terminal is provided, the running number of the cloud terminal is determined based on the predicted user usage number of the cloud applications in the cloud application set, and a corresponding number of cloud terminals are run in advance, so that the user does not have to rent the cloud terminal based on a fixed rental mode, but can flexibly deploy a corresponding number of cloud terminals according to the actual usage demand of the user for the cloud terminal, thereby improving the running flexibility of the cloud terminal and the utilization rate of the cloud terminal resources.

[0010] It should be understood that the content described in this part is not intended to identify key or important features of the embodiments of the present disclosure, nor to limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0011] The accompanying drawings are used to better understand the present scheme, and do not constitute a limitation on the present disclosure. Among them:

[0012] Figure 1 is an exemplary system architecture diagram to which an embodiment according to the present disclosure can be applied;

[0013] Figure 2 is a flowchart of an embodiment of the scheduling method of the cloud terminal according to the present disclosure;

[0014] Figure 3 is a schematic diagram of the relationship between the cloud terminal and the user in the prior art according to the present embodiment;

[0015] Figure 4 is a schematic diagram of the relationship between the cloud terminal and the user according to the present embodiment;

[0016] Figure 5 is a schematic diagram of the application scenario of the scheduling method of the cloud terminal according to the present embodiment;

[0017] Figure 6 is a flowchart of another embodiment of the scheduling method of the cloud terminal according to the present disclosure;

[0018] Figure 7 is a structural diagram of an embodiment of the scheduling device of the cloud terminal according to the present disclosure;

[0019] Figure 8 is a structural schematic diagram of a computer system suitable for implementing the embodiments of the present disclosure. DETAILED DESCRIPTION

[0020] Exemplary embodiments of the present disclosure are described herein with reference to the accompanying drawings, which are meant to be exemplary in nature, and include various details intended to facilitate understanding of the present disclosure. Accordingly, it should be understood that various changes and modifications to the embodiments described herein can be made by those having ordinary skill in the art without departing from the scope and spirit of the present disclosure. Also, for the purpose of clarity and the brevity, the description below omits the description of well-known functions and structures.

[0021] In the technical solution of the present disclosure, the collection, storage, use, processing, transmission, provision and disclosure of user personal information comply with relevant laws and regulations and do not violate public order and good customs.

[0022] Figure 1 An exemplary architecture 100 of the scheduling method and device of the cloud terminal to which the present disclosure can be applied is shown.

[0023] As shown in Figure 1 The system architecture 100 can include terminal devices 101, 102, 103, a network 104 and a server 105. The terminal devices 101, 102, 103 are communicatively connected to form a topology network, and the network 104 is used as a medium to provide a communication link between the terminal devices 101, 102, 103 and the server 105. The network 104 can include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0024] The terminal devices 101, 102, 103 can be hardware devices or software that support network connection to interact and process data. When the terminal devices 101, 102, 103 are hardware, they can be various electronic devices that support network connection, information acquisition, interaction, display, processing, etc., including but not limited to smart phones, tablet computers, e-book readers, laptop computers and desktop computers, etc. When the terminal devices 101, 102, 103 are software, they can be installed in the above-mentioned electronic devices. They can be implemented as multiple software or software modules for providing distributed services, or as a single software or software module. No specific limitation is made herein.

[0025] The server 105 can be a server that provides various services, such as a background processing server that provides cloud terminal services to users to which the terminal devices 101, 102, 103 belong. The server can determine the number of cloud terminals to run based on the predicted number of users using the cloud applications in the cloud application set, and run a corresponding number of cloud terminals in advance. As an example, the server 105 can be a cloud server.

[0026] It should be noted that a server can be either hardware or software. When the server is hardware, it can be implemented as a distributed server cluster consisting of multiple servers, or as a single server. When the server is software, it can be implemented as multiple software programs or software modules (such as software programs or software modules used to provide distributed services), or as a single software program or software module. No specific limitations are made here.

[0027] It should also be noted that the cloud terminal scheduling method provided in the embodiments of this disclosure can be executed by the server, by the terminal device, or by the server and the terminal device in cooperation with each other. Accordingly, the various parts (e.g., various units) included in the cloud terminal scheduling device can be all located in the server, all located in the terminal device, or located in the server and the terminal device respectively.

[0028] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included. When the electronic devices on which the cloud terminal's scheduling method runs do not require data transmission with other electronic devices, the system architecture may consist only of the electronic devices (e.g., servers or terminal devices) on which the cloud terminal's scheduling method runs.

[0029] Please refer to Figure 2 , Figure 2 A flowchart of a cloud terminal scheduling method provided in this disclosure embodiment, wherein process 200 includes the following steps:

[0030] Step 201: Based on the historical operation data of the cloud applications in the cloud application set, predict the number of users using the cloud applications in the cloud application set within a preset time period.

[0031] In this embodiment, the execution entity of the cloud terminal scheduling method (e.g., Figure 1 Terminal devices or servers in the cloud application set can obtain historical operation data of cloud applications in the cloud application set remotely or locally through wired network connection or wireless network connection, and predict the number of users using cloud applications in the cloud application set within a preset time period based on the historical operation data of cloud applications in the cloud application set.

[0032] The cloud application suite includes various cloud applications, including but not limited to social cloud applications, news cloud applications, gaming cloud applications, e-commerce cloud applications, and utility cloud applications. These implementing entities are based on virtualization technology and can provide users with cloud terminal services such as cloud phones and cloud computers, allowing users to deploy various cloud applications they need on these cloud terminals.

[0033] According to the time period in which each user uses each cloud application of the cloud terminal according to historical running data, the execution subject can predict the trend of the number of users using each cloud application in the future, and thus predict the number of users using each cloud application in the cloud application set in the preset time period according to the trend.

[0034] In order to further improve the prediction accuracy of the number of users, the execution subject needs to further consider whether the cloud application has been updated recently, whether there is a preferential activity for the cloud application in the preset time period, whether the preset time period is a holiday, and other key factors affecting the number of users in the prediction process.

[0035] In some optional implementations of the embodiment, the execution subject can perform the step 201 by using a pre-trained prediction model to predict the number of users using each cloud application in the cloud application set in the preset time period according to the historical running data of each cloud application in the cloud application set.

[0036] The prediction model is used to represent the corresponding relationship between the historical running data of the cloud application and the number of users using the cloud application in the preset time period. The prediction model can be trained by using a neural network model, for example, the prediction model can be a recurrent neural network model or an LSTM (long short-term memory) model.

[0037] As an example, the prediction model can be trained by first obtaining a training sample set. The training sample in the training sample set includes the historical running data of the cloud application, data representing whether the application has been updated recently, whether there is a preferential activity for the cloud application in the preset time period, whether the preset time period is a holiday, and other key factors affecting the number of users, and a prediction label representing the number of users using the cloud application in the future time period.

[0038] The future time period corresponding to the prediction label can be a future time period relative to the historical time period corresponding to the historical running data. Specifically, the execution subject divides all historical running data of the cloud application up to the present into two parts, one part as historical running data in the training sample, and the other part as a prediction label. The running data corresponding to the prediction label is after the time corresponding to the historical running data. As an example, for the cloud application A, the execution subject obtains the running data of the past 30 days, and uses the running data of the previous 25 days as historical running data and the number of users using the running data in the following 5 days as a prediction label.

[0039] Then, a machine learning method is used to take historical running data of the cloud application as input of an initial prediction model, and take a prediction label corresponding to the training data as an expected output, to train the prediction model.

[0040] In this implementation, based on the trained prediction model, the user usage quantity of the cloud application in the cloud application set in the preset time period is predicted, and the prediction accuracy of the user usage quantity is improved.

[0041] In step 202, the running quantity of the cloud terminal running in the preset time period is determined according to the user usage quantity.

[0042] In this embodiment, the execution subject can determine the running quantity of the cloud terminal running in the preset time period according to the user usage quantity.

[0043] As an example, for each cloud application in the cloud application set, the user usage quantity corresponding to the cloud application can be directly taken as the running quantity of the cloud terminal running the cloud application in the preset time period.

[0044] As another example, for each cloud application in the cloud application set, the product of the user usage quantity corresponding to the cloud application and a preset proportion can be taken as the running quantity of the cloud terminal running the cloud application in the preset time period. The preset proportion corresponding to each cloud application can be set according to actual conditions, and the preset proportions corresponding to different cloud applications can be the same or different, which is not limited herein.

[0045] In some optional implementations of this embodiment, the execution subject can execute the above step 202 in the following manner:

[0046] First, the initial running quantity of the cloud terminal running the cloud application in the cloud application set in the preset time period is determined according to the user usage quantity of the cloud application in the cloud application set.

[0047] As an example, for each cloud application in the cloud application set, the user usage quantity corresponding to the cloud application can be directly taken as the initial running quantity of the cloud terminal running the cloud application in the preset time period.

[0048] Second, the running quantity of the cloud terminal running the cloud application in the cloud application set in the preset time period is determined according to the initial running quantity and the attribute information of the cloud application in the cloud application set.

[0049] The attribute information of the cloud application can be information such as required storage space of the cloud application and required installation time of the cloud application.

[0050] Specifically, a plurality of attribute thresholds can be set in ascending order, and different preset proportions are set for applications in different attribute ranges according to the principle that the attribute range size is positively correlated with the preset proportion size. For each cloud application in the cloud application set, the product of the initial running number of the cloud application corresponding to the cloud application and the preset proportion can be taken as the running number of the cloud terminal running the cloud application in the preset time period.

[0051] Taking the storage space required by the cloud application as an example, a first storage threshold and a second storage threshold can be set. The first storage threshold is smaller than the second storage threshold. For a cloud application with a storage space in a range smaller than the first storage threshold, the preset proportion is set to 50%; for a cloud application with a storage space between the first storage threshold and the second storage threshold, the preset proportion is set to 75%; and for a cloud application with a storage space in a range larger than the second storage threshold, the preset proportion is set to 100%.

[0052] When the storage space of the cloud application is small, the cloud terminal can generally install and start the cloud application based on less time, at this time, fewer cloud terminals can be prepared in advance, and even if the number of prepared cloud terminals is insufficient, more cloud terminals can be installed and started in less time to run the cloud application; when the storage space of the cloud application is large, the cloud terminal generally installs and starts the cloud application based on more time, at this time, a sufficient number of cloud terminals need to be prepared in advance to avoid the problem of insufficient preparation and long waiting time of the user.

[0053] In the implementation mode, the attribute information of the cloud application is further considered on the basis of the user usage number of the cloud application to finally determine the running number of the cloud terminal to be prepared, so that the determined running number of the cloud terminal is more in line with the actual running situation of the cloud terminal, and the accuracy of the running number and the flexibility of the cloud terminal running are improved.

[0054] In some optional implementation modes of the embodiment, the execution subject can execute the first step by determining the initial running number of the cloud terminal of the cloud application in the to-be-run cloud application group according to the user usage number of the cloud application in the cloud application set and the usage correlation between different cloud applications in the cloud application set.

[0055] The usage correlation represents the correlation between different cloud applications generated based on the user simultaneously deploying and using different cloud applications in the cloud application set in the cloud terminal, and the cloud application group is determined based on the usage correlation between different cloud applications in the cloud application set.

[0056] As an example, the user deploys both a game cloud application A and a friend-making cloud application B in the cloud terminal during the use of the cloud terminal, and it is considered that the game cloud application A and the friend-making cloud application B have use correlation, and the game cloud application A and the friend-making cloud application B can constitute a cloud application group.

[0057] For cloud applications in the same cloud application group, the cloud applications are generally deployed in the same cloud terminal. In the implementation mode, for cloud applications in a cloud application group having use correlation, the initial running quantity of the cloud terminal running the cloud applications in the cloud application group is determined in combination with the user use quantity of each cloud application in the cloud application group.

[0058] As an example, the user use quantity corresponding to the game cloud application A is 500, the user use quantity corresponding to the friend-making cloud application B is 300, the user use quantity of the tool cloud application C is 300, and the user use quantity of the online shopping cloud application D is 200, and the initial running quantity corresponding to the cloud application group composed of the game cloud application A, the friend-making cloud application B and the tool cloud application C is 300, and the initial running quantity corresponding to the cloud application group composed of the game cloud application A and the online shopping cloud application D is 200.

[0059] In the implementation mode, the use correlation between applications is further considered on the basis of the user use quantity of the cloud application, so that the cloud application group composed of cloud applications having use correlation is taken as a unit to determine the cloud terminal running the cloud applications in the cloud application group, and the accuracy of the determined initial running quantity is further improved.

[0060] In some optional implementation modes of the embodiment, the execution subject can execute the second step in the following manner: for a cloud application group composed of different cloud applications in the cloud application set, the running quantity of the cloud terminal running the cloud applications in the cloud application group in the preset time period is determined according to the initial running quantity of the cloud terminal corresponding to the cloud application group and the attribute information of the cloud applications in the cloud application group.

[0061] As an example, for the maximum value in the attribute information of the cloud applications in the cloud application group or the sum of the attribute information of the cloud applications in the cloud application group, a plurality of attribute thresholds are set in ascending order, and different preset proportions are set for cloud applications in different attribute ranges according to the principle that the attribute range size is positively correlated with the preset proportion size. For each kind of cloud application in the cloud application set, the product of the initial running quantity corresponding to the cloud application and the preset proportion can be taken as the running quantity of the cloud terminal running the cloud application in the preset time period.

[0062] In the implementation, for a cloud application group composed of cloud applications with usage correlation, the attribute information of the cloud applications in the cloud application group is further considered on the basis of the user usage quantity of the cloud applications to finally determine the running quantity of the cloud terminals to be prepared, so that the determined running quantity of the cloud terminals is more consistent with the actual running situation of the cloud terminals, and the accuracy of the running quantity is further improved.

[0063] In step 203, the cloud terminals in the running quantity are pre-run before the preset time period arrives.

[0064] In the embodiment, the execution subject can pre-run the cloud terminals in the running quantity before the preset time period arrives.

[0065] For example, a time difference between the current time and the preset time period is determined, and when the time difference is less than a preset time difference threshold, it is considered that the preset time period is about to arrive, and the cloud terminals in the running quantity are pre-run. The preset time difference threshold can be set according to actual conditions, for example, the preset time difference threshold is 5 minutes.

[0066] Referring back to Figure 3 , a schematic diagram 300 of the relationship between a cloud terminal and a user in the prior art is shown. After a user rents a cloud terminal, the user is bound to the cloud terminal and occupies the cloud terminal in a fixed rental time period, regardless of whether the user uses the cloud terminal or not, which causes waste of cloud terminal resources in an idle state.

[0067] Referring back to Figure 4 , a schematic diagram 400 of the relationship between a cloud terminal and a user in the present disclosure is shown. A user is bound to a cloud terminal and occupies the cloud terminal only in the process of using the cloud terminal. When the cloud terminal is not used, the cloud terminal is applied for use by other users, so that the cloud terminal can be flexibly allocated to users who actually have usage demand, and the utilization rate of cloud terminal resources is improved.

[0068] Referring back to Figure 5 , Figure 5 is an application scenario 500 of the scheduling method of the cloud terminal according to the embodiment. In the application scenario of Figure 5 , a server 501 first obtains historical running data 503 of cloud applications in a cloud application set from a database 502. Then, according to the historical running data of the cloud applications in the cloud application set, the user usage quantity 504 of the cloud applications in the cloud application set in a preset time period is predicted; then, according to the user usage quantity 504, the running quantity 505 of the cloud terminals running in the preset time period is determined; finally, the cloud terminals in the running quantity are pre-run before the preset time period arrives.

[0069] In this embodiment, a cloud terminal scheduling method is provided. The running number of cloud terminals is determined based on the predicted user usage number of cloud applications in a cloud application set, and a corresponding number of cloud terminals are pre-run. The user does not have to rent cloud terminals based on a fixed rental mode, but can flexibly deploy a corresponding number of cloud terminals according to the actual usage demand of the user for the cloud terminals, thereby improving the running flexibility of the cloud terminals and the utilization rate of cloud terminal resources.

[0070] In some optional implementation manners of this embodiment, the execution subject can further perform the following operation: first, during the use of the cloud terminal by the target user, the target cloud application used by the target user next is predicted according to the historical operation data of the target user up to the present; and then, the target cloud application is preloaded.

[0071] As an example, the execution subject can predict the target cloud application used by the target user next according to the historical operation data of the target user up to the present by using a prediction model. The prediction model can be a neural network model such as a recurrent neural network or an LSTM network.

[0072] The prediction model can be trained in the following manner: first, a training sample set is obtained. The training sample in the training sample set includes historical operation data of a user and a prediction label representing a target cloud application used by the user next. Then, a machine learning method is used to train the prediction model, taking the historical operation data of the user in the training sample as input and taking the prediction label corresponding to the input data as expected output.

[0073] In this implementation manner, the cloud application operated by the target user next is preloaded based on the prediction of the cloud application to be operated by the target user, which can reduce the waiting time of the user and improve the user experience.

[0074] In some optional implementation manners of this embodiment, the execution subject can further perform the following operation: first, the use time of the cloud terminal by the target user is determined; and then, the fee information of the cloud terminal used by the target user is determined according to the use time.

[0075] In this implementation manner, the use time determined is the actual use time of the cloud terminal by the user, and the fee information of the cloud terminal used by the user is determined based on the use time, which is more in line with the actual use situation of the user for the cloud terminal and eliminates the problem of waste of use fees of the user in the fixed rental mode.

[0076] Continuing to refer to Figure 6 FIG. 6 shows a schematic flow 600 of yet another embodiment of a cloud terminal scheduling method according to the present disclosure, including the following steps:

[0077] Step 601: Using a pre-trained prediction model, predict the number of users using the cloud applications in the cloud application set within a preset time period based on the historical operation data of the cloud applications in the cloud application set.

[0078] The prediction model is used to characterize the correspondence between the historical operational data of cloud applications and the number of users using cloud applications within a preset time period.

[0079] Step 602: Determine the initial number of cloud terminals running the cloud applications in the cloud application group to be run, based on the number of users using the cloud applications in the cloud application set and the usage correlation between different cloud applications in the cloud application set.

[0080] Among them, the association representation is used, based on the association between different cloud applications generated when a user simultaneously deploys and uses different cloud applications in the cloud application set on the cloud terminal. The cloud application group is determined based on the usage association between different cloud applications in the cloud application set.

[0081] Step 603: For cloud application groups composed of different cloud applications in the cloud application set, determine the number of cloud terminals running the cloud applications in the cloud application group within a preset time period based on the initial number of running cloud terminals corresponding to the cloud application group and the attribute information of the cloud applications in the cloud application group.

[0082] Step 604: Before the preset time period arrives, pre-run the above-mentioned number of cloud terminals.

[0083] Step 605: During the process of the target user using the cloud terminal, predict the target cloud application that the target user will use next based on the target user's historical operation data up to the present.

[0084] Step 606: Preload the target cloud application.

[0085] Step 607: Determine the usage time of the target user's cloud terminal.

[0086] Step 608: Determine the cost information for the target user's use of the cloud terminal based on the usage time.

[0087] As can be seen from this embodiment, with Figure 2 Compared with the corresponding embodiments, the cloud terminal scheduling method in this embodiment specifically describes the process of determining the number of running applications, the preloading process of the target cloud application, and the process of determining the cost information of the target user using the cloud terminal. This further improves the operational flexibility of the cloud terminal, the utilization rate of cloud terminal resources, and saves the user's usage costs.

[0088] Continue to refer to Figure 7As an implementation of the method shown in the above figures, the disclosure provides an embodiment of a scheduling device of a cloud terminal, which corresponds to the method embodiment shown in Figure 2 The device can be applied in various electronic devices.

[0089] As shown in Figure 7 The scheduling device 700 of the cloud terminal includes a first prediction unit 701 configured to predict, according to historical running data of cloud applications in a cloud application set, a user usage quantity of the cloud applications in the cloud application set within a preset time period; a first determination unit 702 configured to determine, according to the user usage quantity, a running quantity of the cloud terminal running within the preset time period; and a running unit 703 configured to pre-run the cloud terminal of the running quantity before the preset time period arrives.

[0090] In some optional implementations of the embodiment, the first prediction unit 701 is further configured to predict, according to the historical running data of the cloud applications in the cloud application set, the user usage quantity of the cloud applications in the cloud application set within the preset time period by a pre-trained prediction model, where the prediction model is used to represent a corresponding relationship between the historical running data of the cloud applications and the user usage quantity of the cloud applications within the preset time period.

[0091] In some optional implementations of the embodiment, the first determination unit 702 is further configured to determine, according to the user usage quantity of the cloud applications in the cloud application set, an initial running quantity of the cloud terminal running the cloud applications in the cloud application set within the preset time period; and determine, according to the initial running quantity and attribute information of the cloud applications in the cloud application set, the running quantity of the cloud terminal running the cloud applications in the cloud application set within the preset time period.

[0092] In some optional implementations of the embodiment, the first determination unit 702 is further configured to determine, according to the user usage quantity of the cloud applications in the cloud application set and usage correlation between different cloud applications in the cloud application set, an initial running quantity of the cloud terminal running the cloud applications in a cloud application group to be run, where the usage correlation represents correlation between different cloud applications generated based on simultaneous deployment and use of the different cloud applications in the cloud application set in the cloud terminal, and the cloud application group is determined based on the usage correlation between the different cloud applications in the cloud application set.

[0093] In some optional implementations of the embodiment, the first determination unit 702 is further configured to, for a cloud application group composed of different cloud applications in the cloud application set, determine, according to the initial running quantity of the cloud terminal corresponding to the cloud application group and attribute information of the cloud applications in the cloud application group, a running quantity of the cloud terminal running the cloud applications in the cloud application group within the preset time period.

[0094] In some optional implementation of the embodiment, the apparatus further comprises a second prediction unit (not shown in the figure) configured to predict a target cloud application used by the target user next according to historical operation data of the target user up to the present during the use of the cloud terminal by the target user; and a preloading unit (not shown in the figure) configured to preload the target cloud application.

[0095] In some optional implementation of the embodiment, the apparatus further comprises a second determination unit (not shown in the figure) configured to determine a use time of the cloud terminal by the target user; and a third determination unit (not shown in the figure) configured to determine cost information of the cloud terminal used by the target user according to the use time.

[0096] In the embodiment, a scheduling apparatus of a cloud terminal is provided, the running number of the cloud terminal is determined based on the predicted user usage number of the cloud application in the cloud application set, and a corresponding number of cloud terminals are pre-run, so that the user does not have to rent the cloud terminal based on a fixed rental mode, but can flexibly deploy a corresponding number of cloud terminals according to the actual use demand of the user for the cloud terminal, thereby improving the running flexibility of the cloud terminal and the utilization rate of the cloud terminal resources.

[0097] According to the embodiments of the present disclosure, the present disclosure further provides an electronic device, comprising: at least one processor; and a memory connected with the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to implement the cloud terminal scheduling method described in any of the above embodiments.

[0098] According to the embodiments of the present disclosure, the present disclosure further provides a readable storage medium, which stores computer instructions for enabling a computer to implement the cloud terminal scheduling method described in any of the above embodiments when the computer executes the computer instructions.

[0099] The present disclosure provides a computer program product, which can implement the cloud terminal scheduling method described in any of the above embodiments when executed by a processor.

[0100] Figure 8A schematic block diagram of an example electronic device 800 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0101] like Figure 8 As shown, device 800 includes a computing unit 801, which can perform various appropriate actions and processes based on a computer program stored in read-only memory (ROM) 802 or a computer program loaded from storage unit 808 into random access memory (RAM) 803. RAM 803 may also store various programs and data required for the operation of device 800. The computing unit 801, ROM 802, and RAM 803 are interconnected via bus 804. Input / output (I / O) interface 805 is also connected to bus 804.

[0102] Multiple components in device 800 are connected to I / O interface 805, including: input unit 806, such as keyboard, mouse, etc.; output unit 807, such as various types of monitors, speakers, etc.; storage unit 808, such as disk, optical disk, etc.; and communication unit 809, such as network card, modem, wireless transceiver, etc. Communication unit 809 allows device 800 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0103] The computing unit 801 can be various general and / or special purpose processing components with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 801 performs various methods and processes described above, such as the cloud terminal scheduling method. For example, in some embodiments, the cloud terminal scheduling method can be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 808. In some embodiments, part or all of the computer program can be loaded and / or installed onto the device 800 via the ROM 802 and / or the communication unit 809. When the computer program is loaded onto the RAM 803 and executed by the computing unit 801, one or more steps of the cloud terminal scheduling method described above can be performed. Alternatively, in other embodiments, the computing unit 801 can be configured to perform the cloud terminal scheduling method by any other suitable means, such as by means of firmware.

[0104] Various implementations of the systems and techniques described above can be realized in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a programmable logic device (PLD), a computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0105] Program code for carrying out methods of the present disclosure can be written in any combination of one or more programming languages. The program code can be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the program code, when executed by the processor or controller, produces the functions / operations specified in the flowcharts and / or the block diagrams. The program code can be executed entirely on a machine, partially on a machine, partially on a machine as a stand-alone software package, partially on a machine and partially on a remote machine or entirely on a remote machine or server.

[0106] In the context of this disclosure, a machine-readable medium can be a tangible medium that contains or stores a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include but is not limited to an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of the machine-readable storage medium will include one or more lines of electrical connections, portable computer disks, hard disk drives, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or Flash memory), optical fibers, portable compact disc read-only memories (CD-ROMs), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0107] To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0108] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0109] The computer system can include clients and servers. This relationship can be remote, such as over a network, or via a cloud server. The servers can be cloud servers, also known as cloud computing servers or cloud hosts, which are a host product in the cloud computing service system to solve the defects of large management difficulty and weak business scalability in traditional physical hosts and virtual private server (VPS) services. The servers can also be servers of a distributed system or servers combined with a blockchain.

[0110] According to the technical scheme of the embodiment of the present disclosure, a cloud terminal scheduling method is provided. The number of running cloud terminals is determined based on the predicted number of users using the cloud applications in the cloud application set, and a corresponding number of cloud terminals are pre-run. The user does not have to rent cloud terminals based on a fixed rental mode, but can flexibly deploy a corresponding number of cloud terminals according to the actual use demand of the user for the cloud terminals, thereby improving the running flexibility of the cloud terminals and the utilization rate of cloud terminal resources.

[0111] It should be understood that the various forms of the flow shown above can be reordered, added, or deleted steps. For example, the steps described in the present disclosure can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical scheme provided by the present disclosure can be achieved, and the present disclosure is not limited herein.

[0112] The above detailed description does not constitute a limitation on the protection scope of the present disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements and improvements within the spirit and principles of the present disclosure should be included in the protection scope of the present disclosure.

Claims

1. A method for scheduling cloud terminals, comprising: predicting, according to historical running data of cloud applications in a cloud application set, a number of users of the cloud applications in the cloud application set in a preset time period; determining, according to the number of users of the cloud applications in the cloud application set and a usage correlation between different cloud applications in the cloud application set, an initial number of cloud terminals running cloud applications in a cloud application group in the preset time period, wherein the usage correlation represents a correlation between the different cloud applications generated based on a user deploying and using the different cloud applications in the cloud application set simultaneously in a cloud terminal, and the cloud application group is determined based on the usage correlation between the different cloud applications in the cloud application set; determining, according to the initial number and attribute information of the cloud applications in the cloud application set, a number of cloud terminals running the cloud applications in the cloud application set in the preset time period; running the number of cloud terminals in advance before the preset time period arrives.

2. The method of claim 1, wherein, The predicting, according to the historical running data of the cloud applications in the cloud application set, the number of users of the cloud applications in the cloud application set in the preset time period, comprises: predicting, according to the historical running data of the cloud applications in the cloud application set, the number of users of the cloud applications in the cloud application set in the preset time period by a pre-trained prediction model, wherein the prediction model is used to represent a corresponding relationship between the historical running data of the cloud applications and the number of users of the cloud applications in the preset time period.

3. The method of claim 1, wherein, The determining, according to the initial number and the attribute information of the cloud applications in the cloud application set, the number of cloud terminals running the cloud applications in the cloud application set in the preset time period, comprises: for a cloud application group composed of different cloud applications in the cloud application set, determining, according to the initial number of cloud terminals corresponding to the cloud application group and the attribute information of the cloud applications in the cloud application group, the number of cloud terminals running the cloud applications in the cloud application group in the preset time period.

4. The method of any one of claims 1-3, wherein, Further comprising: predicting, according to historical operation data of a target user up to the present, a target cloud application to be used by the target user next time in a process in which the target user uses a cloud terminal; preloading the target cloud application.

5. The method of claim 4, wherein, Further comprising: determining a usage time of the target user using the cloud terminal; determining, according to the usage time, cost information of the target user using the cloud terminal. 6.A device for scheduling cloud terminals, comprising: a first prediction unit configured to predict, according to historical running data of cloud applications in a cloud application set, a number of users of the cloud applications in the cloud application set in a preset time period; The first determining unit is configured to determine an initial running number of cloud terminals running cloud applications in the cloud application group in the preset time period according to the number of user uses of the cloud applications in the cloud application set and the use correlation between different cloud applications in the cloud application set, wherein the use correlation represents the correlation between different cloud applications generated based on the simultaneous deployment and use of the different cloud applications in the cloud application set by a user in a cloud terminal, and the cloud application group is determined based on the use correlation between different cloud applications in the cloud application set; and determine a running number of cloud terminals running the cloud applications in the cloud application set in the preset time period according to the initial running number and attribute information of the cloud applications in the cloud application set. The running unit is configured to pre-run the running number of cloud terminals before the preset time period arrives.

7. The apparatus of claim 6, wherein, The first prediction unit is further configured to: predict the number of user uses of the cloud applications in the cloud application set in the preset time period according to historical running data of the cloud applications in the cloud application set by using a pre-trained prediction model, wherein the prediction model is used to represent the corresponding relationship between the historical running data of the cloud applications and the number of user uses of the cloud applications in the preset time period.

8. The apparatus of claim 6, wherein, The first determining unit is further configured to: for a cloud application group composed of different cloud applications in the cloud application set, determine a running number of cloud terminals running the cloud applications in the cloud application group in the preset time period according to the initial running number of the cloud terminals corresponding to the cloud application group and the attribute information of the cloud applications in the cloud application group.

9. The apparatus of any one of claims 6-8, wherein, Further comprising: a second prediction unit configured to predict a target cloud application used by a target user in the next step according to historical operation data of the target user up to the present during the use of the cloud terminal by the target user; a preloading unit configured to pre-load the target cloud application.

10. The apparatus of claim 9, wherein, Further comprising: a second determining unit configured to determine a use time of the cloud terminal by the target user; a third determining unit configured to determine fee information of the cloud terminal used by the target user according to the use time.

11. An electronic device, comprising: comprising: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-5.

12. A non-transitory computer-readable storage medium having stored thereon computer instructions, wherein, The computer instructions are used to enable the computer to perform the method of any one of claims 1-5.

13. A computer program product, comprising: A computer program, when executed by a processor, implements the method of any one of claims 1-5.

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