KR20230112941A - System for cloud streaming service, method of cloud streaming service based on selective video processing considering resource and load and apparatus for the same - Google Patents

Abstract

A cloud streaming service system, a cloud streaming service method based on selective video processing considering resources and loads, and an apparatus therefor are disclosed. The cloud streaming server according to the present invention generates a performance profile of an application based on a load test, predicts a behavior of the next time for the application, generates load data at the next time corresponding to the application, and generates the performance profile, the next time. A processor that selects a mode for processing a video corresponding to a current view in consideration of view load data and a system load of the current view, processes the video in the selected mode, and streams the video to a user's terminal; and a memory for storing the performance profile and the next load data.

Description

Cloud streaming service method based on selective video processing considering cloud streaming service system, resource and load, and apparatus therefor

The present invention relates to a cloud streaming service system, a cloud streaming service method based on selective video processing considering resources and loads, and an apparatus therefor, and in particular, a cloud that can efficiently use software and hardware resources of a cloud streaming server to the maximum extent. It relates to a cloud streaming service method based on selective video processing considering a streaming service system, resources and load, and an apparatus therefor.

A cloud computing service is a service in which a user stores content in a cloud storage on the Internet, not in a local storage, and accesses the cloud storage anytime, anywhere using an Internet-enabled device to use the content.

In a cloud streaming service system, it is common to use a fixed resource of either software or hardware in order to process a video in a server and stream it to a client. However, a situation in which CPU or GPU resources are insufficient occurs due to the number of concurrent users connected to the cloud streaming server, the situation in which several applications are running in the cloud streaming server, or the resources used according to the execution time of the application.

However, conventionally, since video is decoded and rendered through fixed resources in software or hardware, variable processing for video processing cannot be performed even in situations where CPU or GPU resources are insufficient, so CPU or GPU resources can be used as much as possible. The downside was that it could not be used on the road.

Korean Patent Publication No. 10-2016-0084216, published on July 13, 2016 (Name: Cloud streaming service system, cloud streaming service method using service quality control and apparatus therefor)

An object of the present invention is to use resources within the system to the maximum limit by performing a process for providing a service by selectively using software and hardware resources supported by the cloud streaming service system.

In addition, an object of the present invention is to perform a process (decoding and rendering, etc.) for service provision more efficiently by considering the load of the next time that is predicted in consideration of the performance profile of the application serviced through the cloud streaming server.

Another object of the present invention is to provide services to more users by increasing the number of simultaneous access to the cloud streaming server by efficiently using resources within the cloud streaming service system to the maximum limit.

In order to achieve the above object, the cloud streaming server based on selective video processing in consideration of resources and load according to the present invention generates a performance profile of an application based on a load test, predicts the next behavior of the application, and Next time load data corresponding to the application is generated, a mode (MODE) for processing a video corresponding to the current time is selected in consideration of the performance profile, the next time load data, and the system load at the current time, and the selected mode a processor for processing the video and streaming it to a user's terminal; and a memory for storing the performance profile and the next load data.

In this case, the mode may correspond to any one of a software mode and a hardware mode.

In this case, the performance profile may correspond to data obtained by measuring the load of the application at each point in time in the software mode and the hardware mode, respectively.

In this case, the next time load data may include information about a software mode load corresponding to the behavior at the next time point and a hardware mode load corresponding to the behavior at the next time point among the loads for each time point.

At this time, the processor may select a mode for processing the video by comparing system resource efficiency when the video is processed in the software mode and system resource efficiency when the video is processed in the hardware mode.

In this case, the mode may be changed from the software mode to the hardware mode or from the hardware mode to the software mode according to system resource efficiency.

In this case, the software mode may operate based on the GPU, and the hardware mode may operate based on the CPU.

In addition, the cloud streaming service method based on selective video processing in consideration of resources and load according to the present invention for achieving the above object, generating a performance profile of the application based on a load test; generating next-time load data corresponding to the application by predicting next-time behavior for the application; selecting a mode for processing a video corresponding to a current view in consideration of the performance profile, the load data of the next view, and the system load of the current view; and processing the video in the selected mode and streaming it to the user's terminal.

In this case, the mode may correspond to any one of a software mode and a hardware mode.

In this case, the performance profile may correspond to data obtained by measuring the load of the application at each point in time in the software mode and the hardware mode, respectively.

In this case, the next time load data may include information about a software mode load corresponding to the behavior at the next time point and a hardware mode load corresponding to the behavior at the next time point among the loads for each time point.

In this case, a mode for processing the video may be selected by comparing system resource efficiency when the video is processed in the software mode and system resource efficiency when the video is processed in the hardware mode.

In this case, the mode may be changed from the software mode to the hardware mode or from the hardware mode to the software mode according to system resource efficiency.

In this case, the software mode may operate based on the GPU, and the hardware mode may operate based on the CPU.

In addition, the cloud streaming service system according to the present invention for achieving the above object, generates a performance profile of the application based on the load test, predicts the next time behavior for the application, and the next time load corresponding to the application It generates data, selects a mode (MODE) for processing a video corresponding to the current time in consideration of the performance profile, the load data at the next time, and the system load at the current time, and processes the video in the selected mode so that the user Cloud streaming server for streaming to the terminal of; and a user terminal receiving and outputting the video processed in the selected mode.

According to the present invention, it is possible to use resources in the system to the maximum limit by performing a process for providing a service by selectively using software and hardware resources supported by the cloud streaming service system.

In addition, the present invention can more efficiently perform processing processes (decoding and rendering, etc.) for service provision by considering the load of the next time that is predicted by considering the performance profile of the application serviced through the cloud streaming server.

In addition, the present invention can provide services to more users by increasing the number of simultaneous access to the cloud streaming server by efficiently using resources within the cloud streaming service system to the maximum limit.

1 is a block diagram showing a cloud streaming service system according to an embodiment of the present invention.
Figure 2 is an operational flow chart showing a cloud streaming service method based on selective video processing in consideration of resources and load according to an embodiment of the present invention.
3 is a block diagram showing an example of a cloud streaming server based on selective video processing considering resources and loads according to the present invention.
4 is a diagram showing an example of a performance profile of an application according to the present invention.
5 is a diagram showing an example of a cloud streaming service process based on selective video processing considering resources and loads according to the present invention.
6 is an operation flow diagram showing in detail a cloud streaming service method based on selective video processing considering resources and loads according to an embodiment of the present invention.
7 is an operational flowchart showing in detail a process of selecting a mode of the cloud streaming service method according to the present invention.
8 is a diagram illustrating a cloud streaming server based on selective video processing considering resources and loads according to an embodiment of the present invention.

The present invention will be described in detail with reference to the accompanying drawings. Here, repeated descriptions, well-known functions that may unnecessarily obscure the subject matter of the present invention, and detailed descriptions of configurations are omitted. Embodiments of the present invention are provided to more completely explain the present invention to those skilled in the art. Accordingly, the shapes and sizes of elements in the drawings may be exaggerated for clarity.

Hereinafter, preferred embodiments according to the present invention will be described in detail with reference to the accompanying drawings.

1 is a block diagram showing a cloud streaming service system according to an embodiment of the present invention.

Referring to FIG. 1, the cloud streaming service system according to an embodiment of the present invention includes a cloud streaming server 110, terminal devices 120-1 to 120-N and a network 130.

Cloud streaming server 110 generates a performance profile of the application based on the load test.

In this case, the performance profile may correspond to data obtained by measuring load for each time point of the application in a software mode and a hardware mode, respectively.

In addition, the cloud streaming server 110 generates next-time load data corresponding to the application by predicting the next-time action for the application.

At this time, the next time load data includes information about a software mode load corresponding to a next time action among loads at each time point and a hardware mode load corresponding to a next time time action.

In addition, the cloud streaming server 110 selects a mode (MODE) for processing a video corresponding to the current view in consideration of the performance profile, the load data of the next view, and the system load of the current view.

In this case, the mode may correspond to any one of a software mode and a hardware mode.

In this case, the software mode may operate based on the GPU, and the hardware mode may operate based on the CPU.

In this case, a mode for processing a video may be selected by comparing system resource efficiency when the video is processed in the software mode and system resource efficiency when the video is processed in the hardware mode.

In addition, the cloud streaming server 110 processes the video in the selected mode and streams it to the user's terminal devices 120-1 to 120-N.

In this case, the mode may be changed from the software mode to the hardware mode or from the hardware mode to the software mode according to system resource efficiency.

The terminal devices 120-1 to 120-N receive an application execution screen corresponding to the cloud streaming service from the cloud streaming server 110 and provide it to the user.

In addition, the terminal devices 120-1 to 120-N are devices connected to a communication network and capable of executing applications based on a cloud computing system, and are not limited to mobile communication terminals, but are all information communication devices, multimedia terminals, and wired terminals. , It may be a variety of terminals such as fixed terminals and IP (Internet Protocol) terminals. In addition, the terminal devices 120-1 to 120-N are mobile phones, PMPs (Portable Multimedia Played), MIDs (Mobile Internet Devices), smart phones, desktops, tablet PCs, It may be a mobile terminal having various mobile communication specifications, such as a notebook, a net book, a personal digital assistant (PDA), a smart TV, and an information communication device.

In addition, the terminal devices 120-1 to 120-N receive various information such as numbers and text information, set various functions, and input in connection with function control of the terminal devices 120-1 to 120-N. A signal may be transmitted to the control unit through the input unit. Also, the input units of the terminal devices 120-1 to 120-N may include at least one of a keypad and a touchpad that generate an input signal according to a user's touch or manipulation. At this time, the input unit of the terminal devices 120-1 to 120-N is configured in the form of a single touch panel (or touch screen) together with the display unit of the terminal devices 120-1 to 120-N. Input and display functions can be performed simultaneously. In addition, input devices of the terminal devices 120-1 to 120-N may use any type of input means that may be developed in the future, in addition to input devices such as a keyboard, keypad, mouse, joystick, and the like. In particular, the input unit of the terminal devices 120-1 to 120-N according to the present invention may transmit an input signal for uploading or downloading content based on cloud computing to the control unit of the terminal devices 120-1 to 120-N. there is.

In addition, the display units of the terminal devices 120-1 to 120-N may display information about a series of operation states and operation results generated while the terminal devices 120-1 to 120-N perform functions. Also, the display units of the terminal devices 120-1 to 120-N may display menus of the terminal devices 120-1 to 120-N and user data input by users. Here, the display units of the terminal devices 120-1 to 120-N include a Liquid Crystal Display (LCD), a Thin Film Transistor LCD (TFT-LCD), and a Light Emitting Diode (LED). ), Organic LED (OLED), Active Matrix OLED (AMOLED), Retina Display, Flexible Display and 3 Dimensional Display. can At this time, when the display units of the terminal devices 120-1 to 120-N are configured in the form of a touch screen, the display units of the terminal devices 120-1 to 120-N are the same as those of the terminal devices 120-1 to 120-N. Some or all of the functions of the input unit may be performed. In particular, the display units of the terminal devices 120-1 to 120-N according to the present invention may display information related to the execution of content provided based on cloud computing on the screen.

In addition, the storage unit of the terminal devices 120-1 to 120-N is a device for storing data, includes a main storage device and an auxiliary storage device, and is related to the functional operation of the terminal devices 120-1 to 120-N. You can save the applications you need. The storage unit of the terminal devices 120-1 to 120-N may largely include a program area and a data area. Here, when the terminal devices 120-1 to 120-N activate each function in response to a user's request, the corresponding application programs are executed under the control of the controller to provide each function. In particular, the storage unit of the terminal devices 120-1 to 120-N according to the present invention stores an operating system for booting the terminal devices 120-1 to 120-N, a program for uploading or downloading content based on cloud computing, and the like. can be saved Also, the storage units of the terminal devices 120-1 to 120-N may store a content DB for storing a plurality of contents and information of the terminal devices 120-1 to 120-N. In this case, the content DB includes execution data for executing the content and attribute information about the content, and may store content use information according to content execution. Also, the information of the terminal devices 120-1 to 120-N may include terminal specification information.

In addition, the communication units of the terminal devices 120-1 to 120-N may perform functions for transmitting and receiving data through the cloud streaming server 110 and the network 130. Here, the communication units of the terminal devices 120-1 to 120-N may include an RF transmitting unit for up-converting and amplifying the frequency of a transmitted signal, and an RF receiving unit for low-noise amplifying a received signal and down-converting the frequency. there is. The communication unit of the terminal devices 120-1 to 120-N may include at least one of a wireless communication module and a wired communication module. Further, the wireless communication module is a component for transmitting and receiving data according to a wireless communication method, and when the terminal devices 120-1 to 120-N use wireless communication, a wireless network communication module, a wireless LAN communication module, and a wireless fan communication. Data can be transmitted and received to the cloud streaming server 110 using any one of the modules. In addition, the wired communication module is for transmitting and receiving data by wire. The wired communication module may access the network 130 through a wire and transmit/receive data to the cloud streaming server 110 . That is, the terminal devices 120-1 to 120-N access the network 130 using a wireless communication module or a wired communication module, and can transmit and receive data with the cloud streaming server 110 through the network 130. . In particular, the network 130 according to the present invention communicates with the cloud streaming server 110 or other terminal devices 120-1 to 120-N to transmit and receive data necessary for uploading or downloading content based on cloud computing. .

In addition, the control unit of the terminal devices 120-1 to 120-N may be an operating system (OS, Operation System) and a process device that drives each component. For example, the control unit is a cloud streaming server 110 When accessing the cloud streaming server 110 through a separate service application, it is possible to control the entire process of running the service application according to the user's request, and at the same time as the execution, the cloud streaming server 110 A service use request may be transmitted to the streaming server 110, and at this time, information of the terminal devices 120-1 to 120-N necessary for user authentication may be transmitted together.

In addition, the controllers of the terminal devices 120-1 to 120-N may execute specific content stored in the storage units of the terminal devices 120-1 to 120-N according to a user's request. In this case, the controller may store a content use history according to content execution as content use information.

In addition, the control unit of the terminal devices 120-1 to 120-N transmits execution data for executing the content, content information including attribute information about the content, and content use information, which is information based on the content use history, together in the cloud. It can be uploaded by transmitting to the streaming server 110. Thereafter, the control unit may delete the content uploaded to the cloud streaming server 110 from the storage unit of the terminal devices 120-1 to 120-N according to the user's request, and access the cloud streaming server 110. By doing so, content may be executed and used through the cloud streaming server 110 .

In addition, the control unit of the terminal devices 120-1 to 120-N accesses the cloud streaming server 110, downloads content from other terminal devices 120-1 to 120-N, and then controls the content to be stored in the storage unit. Also, when content is executed through the cloud streaming server 110, only data necessary for execution may be received and then the content may be controlled to be executed.

The network 130 provides a passage through which data is transmitted between the cloud streaming server 110 and terminal devices 120-1 to 120-N, a concept encompassing both existing networks and networks that can be developed in the future. am. For example, the network 130 may include a wired and wireless local area communication network that provides communication between various information devices within a limited area, a mobile communication network that provides communication between mobile bodies and between mobile bodies and the outside of the mobile body, and communication between earth stations and earth stations using satellites. It may be a satellite communication network that provides, or any one of wired and wireless communication networks, or a combination of two or more. Meanwhile, the transmission method standards of the network 130 are not limited to existing transmission method standards, and may include all transmission method standards to be developed in the future. In addition, the network used between the cloud streaming server 110 and the terminal devices 120-1 to 120-N in FIG. 1 may be different from the network used between the terminal devices 120-1 to 120-N. and may be the same.

By providing a cloud streaming service through such a cloud streaming service system, by selectively using software resources and hardware resources supported by the cloud streaming service system to perform processing for providing the service, resources within the system can be used to the maximum extent. there is.

In addition, processing for service provision (decoding and rendering, etc.) can be performed more efficiently by considering the load of the next time that is predicted by considering the performance profile of the application serviced through the cloud streaming server.

In addition, by efficiently using resources within the cloud streaming service system to the maximum limit, it is possible to provide services to more users by increasing the number of concurrent users to the cloud streaming server.

Figure 2 is an operational flow chart showing a cloud streaming service method based on selective video processing in consideration of resources and load according to an embodiment of the present invention.

Referring to FIG. 2 , the cloud streaming service method based on selective video processing considering resources and load according to an embodiment of the present invention generates a performance profile of an application based on a load test (S210).

For example, in the cloud streaming server according to an embodiment of the present invention, a performance profile for at least one application executable in the cloud streaming server may be generated through the performance profile generating unit 310 shown in FIG. 3 .

In this case, the performance profile may correspond to data obtained by measuring load for each time point of the application in a software mode and a hardware mode, respectively.

At this time, the software mode may correspond to a mode for decoding and rendering video or content by utilizing software resources such as GPU of the cloud streaming service system, and the hardware mode utilizes hardware resources such as CPU of the cloud streaming service system. This may correspond to a mode for decoding and rendering video or content.

Therefore, in the present invention, in order to perform efficient processing in consideration of resources and loads, data on loads at each point in time of applications for each of software resources and hardware resources may be stored as a database.

For example, a performance profile of an application may be created and stored as a database as shown in FIG. 4 .

At this time, each APP viewpoint shown in FIG. 4 may correspond to a viewpoint corresponding to an application execution screen that is sequentially displayed when an application is executed.

For example, it can be assumed that the time point corresponding to the first execution screen displayed after the application is executed is APP time #1, and APP time #2 or APP time depending on the input generated from the user terminal at APP time #1. The viewpoint can be changed to the execution screen corresponding to #3.

Therefore, in the present invention, the process of decoding, rendering, and streaming the application execution screen for display to the user at each point in time of the application is calculated by calculating the load when processing the software resource and the hardware resource, respectively, and generating the performance profile of the application. can do.

At this time, the performance profile of the application shown in FIG. 4 is an example, and may be created and stored in a database in various forms other than the form shown in FIG. 4 .

In addition, the cloud streaming service method based on selective video processing in consideration of resources and loads according to an embodiment of the present invention predicts the behavior of the next view of the application and generates load data of the next view corresponding to the application (S220).

In this case, the next load data may include information about a software mode load corresponding to a behavior at a next time point and a hardware mode load corresponding to a behavior at a next time point among loads for each time point.

For example, referring to FIG. 4 , if it is assumed that the current time point for an application is APP time #2, the possible next time points are APP time #4, APP time #5, and APP time #6. may apply.

At this time, in the present invention, the application behavior prediction unit 320 shown in FIG. 3 calculates statistics on the point in time at which the application moves after a specific point in time while providing the cloud streaming service, and stores it in a database together with the performance profile. And, using the databased statistics, it is possible to predict the next action.

For example, whenever an application execution is requested from the user, the next point in time shown in FIG. 4 is accumulated and recorded, and based on the accumulated record, a statistical ratio corresponding to the action at the next point in time is calculated. can be calculated

If the behavior of the next time is predicted at APP time #3, the statistic moving from APP time #3 to APP time #7 is compared with the statistic moving from APP time #3 to APP time #8 to obtain a higher percentage of the statistic. The time of having can be predicted as the next time action. Alternatively, when predicting the next action at APP time #4, APP time #9 can be predicted as the next action since there is only one next time point that can be moved from APP time #4.

If the behavior at the next time point is predicted in this way, the next time load data generating unit 330 shown in FIG. 3 processes the predicted next time behavior and the load degree when the predicted next time behavior is processed in the software mode and the predicted next time behavior in the hardware mode. Load data at the next point in time can be created by considering the load degree in the case of

In this case, the next time load data may correspond to data obtained by adding the software mode load and the hardware mode load for the predicted next time behavior to the respective load levels of the software resource and the hardware resource at the current time point.

In this way, by adding the load for the next action to the load at the current time and then using the load data at the time, the selection of a processing mode that can cause overflow due to the action at the next time can be avoided or a more generous processing mode is selected. can be scheduled.

In addition, a cloud streaming service method based on selective video processing considering resources and loads according to an embodiment of the present invention processes a video corresponding to a current view in consideration of a performance profile, next view load data, and system load at the current view. Select a mode (MODE) for (S230).

In this case, the mode may correspond to any one of a software mode and a hardware mode.

In this case, the software mode may operate based on the GPU, and the hardware mode may operate based on the CPU.

For example, in the present invention, the mode selector 340 shown in FIG. 3 complexly considers the performance profile of the previously databased application, the load data at the next time, and the system load measured at the current time, and selects a video at the current time. A mode that is determined to be most efficient for processing can be selected.

In this case, a mode for processing a video may be selected by comparing system resource efficiency when the video is processed in the software mode and system resource efficiency when the video is processed in the hardware mode.

In this case, the system resource efficiency may be determined by considering the occupancy rate of each of the software resource and the hardware resource or the load degree of each of the software resource and the hardware resource.

For example, assuming that software resource occupancy and hardware resource occupancy can be measured from 0 to 100, respectively, the more similar the software resource occupancy value and the hardware resource occupancy value are, the higher the system resource efficiency can be determined.

For another example, assuming that software resource and hardware resource loads can be measured from 0 to 100, respectively, it can be determined that system resource efficiency is higher as the load of software resources and the load of hardware resources are similar.

In addition, in the cloud streaming service method based on selective video processing in consideration of resources and loads according to an embodiment of the present invention, the video is processed in the selected mode and streamed to the user's terminal (S240).

For example, a video may be processed in a mode selected by the streaming unit 350 shown in FIG. 3 and streamed to the user's terminal.

At this time, the user's terminal may receive an application execution screen corresponding to the cloud streaming service from the cloud streaming server, that is, a video processing screen, and provide it to the user.

In this case, the mode may be changed from the software mode to the hardware mode or from the hardware mode to the software mode according to system resource efficiency.

For example, in the present invention, the degree of occupancy or load of software resources and hardware resources is measured for each predetermined measurement period while the cloud streaming service is continuously provided, and if the occupancy or load is excessively high in either direction, The processing mode of the currently provided service may be changed to another processing mode.

By providing a cloud streaming service through a cloud streaming service method based on selective video processing in consideration of such resources and loads, processing is performed to provide a service by selectively using software and hardware resources supported by the cloud streaming service system. By doing this, the resources in the system can be used to the maximum limit.

In addition, processing for service provision (decoding and rendering, etc.) can be performed more efficiently by considering the load of the next time that is predicted by considering the performance profile of the application serviced through the cloud streaming server.

In addition, by efficiently using resources within the cloud streaming service system to the maximum limit, it is possible to provide services to more users by increasing the number of simultaneous access to the cloud streaming server.

5 is a diagram showing an example of a cloud streaming service process based on selective video processing considering resources and loads according to the present invention.

Referring to FIG. 5, the cloud streaming service process based on selective video processing in consideration of resources and loads according to the present invention corresponds to the performance profile 510 generated for the application provided by the cloud streaming server, and the next behavior of the application. This may be performed by collecting or generating information corresponding to the load data 520 at the next time point and the system load 530 at the current time point.

The performance profile 510 may correspond to data obtained by measuring a load for each application in a software mode and a hardware mode, respectively.

For example, the performance profile 510 of the application is calculated by calculating the load when the processing process for decoding, rendering, and streaming the application execution screen for display to the user at each point of the application is processed with software resources and hardware resources, respectively. can be created with

The next load data 520 may include information about a software mode load corresponding to a behavior at a next time point and a hardware mode load corresponding to a behavior at a next time point among loads at each time point.

For example, next time load data is generated by considering the load degree when the behavior of the next time predicted based on the current time of the application is processed in software mode and the load degree when the predicted next time action is processed in hardware mode. can do.

In this case, the next time load data may correspond to data obtained by adding the software mode load and the hardware mode load for the predicted next time behavior to the respective load levels of the software resource and the hardware resource at the current time point.

The system load 530 may correspond to the degree of load measured at the current time in the cloud streaming server.

When all the information corresponding to the performance profile 510, the next time load data 520, and the system load 530 is collected in this way, the mode (MODE) for processing the video requested by the user at the current time is set to the software mode and One of the hardware modes can be selected (S500).

For example, it is determined that it is most efficient to process a video at the current time by considering the performance profile 510 of the databased application, the load data 520 at the next time, and the system load 530 measured at the current time. mode can be selected.

Thereafter, the video may be decoded and rendered through the selected mode and streamed to the user's terminal (S510).

6 is an operation flow diagram showing in detail a cloud streaming service method based on selective video processing considering resources and loads according to an embodiment of the present invention.

Referring to Figure 6, in the cloud streaming service method based on selective video processing in consideration of resources and load according to an embodiment of the present invention, when the application is executed first (S602), the load for each software mode and hardware mode for the application A performance profile of the application may be generated by performing the test (S604) (S606).

For example, in the cloud streaming server according to an embodiment of the present invention, a performance profile for at least one application executable in the cloud streaming server may be generated through the performance profile generating unit 310 shown in FIG. 3 .

In this case, the performance profile may correspond to data obtained by measuring load for each time point of the application in a software mode and a hardware mode, respectively.

At this time, the software mode may correspond to a mode for decoding and rendering video or content by utilizing software resources such as GPU of the cloud streaming service system, and the hardware mode utilizes hardware resources such as CPU of the cloud streaming service system. This may correspond to a mode for decoding and rendering video or content.

Therefore, in the present invention, in order to perform efficient processing in consideration of resources and loads, data on loads at each point in time of applications for each of software resources and hardware resources may be stored as a database.

For example, a performance profile of an application may be created and stored as a database as shown in FIG. 4 .

At this time, each APP viewpoint shown in FIG. 4 may correspond to a viewpoint corresponding to an application execution screen that is sequentially displayed when an application is executed.

For example, it can be assumed that the time point corresponding to the first execution screen displayed after the application is executed is APP time #1, and APP time #2 or APP time depending on the input generated from the user terminal at APP time #1. The viewpoint can be changed to the execution screen corresponding to #3.

Therefore, in the present invention, the process of decoding, rendering, and streaming the application execution screen for display to the user at each point in time of the application is calculated by calculating the load when processing the software resource and the hardware resource, respectively, and generating the performance profile of the application. can do.

At this time, the performance profile of the application shown in FIG. 4 is an example, and may be created and stored in a database in various forms other than the form shown in FIG. 4 .

Thereafter, by predicting the behavior of the application at the next time (S608), load data corresponding to the application at the next time may be generated (S610).

In this case, the next load data may include information about a software mode load corresponding to a behavior at a next time point and a hardware mode load corresponding to a behavior at a next time point among loads for each time point.

For example, referring to FIG. 4 , if it is assumed that the current time point for an application is APP time #2, the possible next time points are APP time #4, APP time #5, and APP time #6. may apply.

At this time, in the present invention, the application behavior prediction unit 320 shown in FIG. 3 calculates statistics on the point in time at which the application moves after a specific point in time while providing the cloud streaming service, and stores it in a database together with the performance profile. And, using the databased statistics, it is possible to predict the next action.

For example, whenever an application execution is requested from the user, the next point in time shown in FIG. 4 is accumulated and recorded, and based on the accumulated record, a statistical ratio corresponding to the action at the next point in time is calculated. can be calculated

If the behavior of the next time is predicted at APP time #3, the statistic moving from APP time #3 to APP time #7 is compared with the statistic moving from APP time #3 to APP time #8 to obtain a higher percentage of the statistic. The time of having can be predicted as the next time action. Alternatively, when predicting the next action at APP time #4, APP time #9 can be predicted as the next action because there is only one next time point that can be moved from APP time #4.

If the behavior at the next time point is predicted in this way, the next time load data generating unit 330 shown in FIG. 3 processes the predicted next time behavior and the load degree when the predicted next time behavior is processed in the software mode and the predicted next time behavior in the hardware mode. Load data at the next point in time can be created by considering the load degree in the case of

At this time, the next time load data may correspond to data obtained by adding the software mode load and the hardware mode load for the predicted next time behavior to the respective load levels of the software resource and the hardware resource at the current time point.

In this way, by adding the load for the next action to the load at the current time and then using the load data at the time, the selection of a processing mode that can cause overflow due to the action at the next time can be avoided or a more generous processing mode is selected. can be scheduled.

Thereafter, the system load at the current time is calculated (S612), and an efficient mode for processing a video corresponding to the current time may be determined in consideration of the performance profile, the load data at the next time, and the system load (S614). ).

At this time, the mode may correspond to any one of a software mode and a hardware mode.

In this case, the software mode may operate based on the GPU, and the hardware mode may operate based on the CPU.

For example, in the present invention, the mode selection unit 340 shown in FIG. 3 complexly considers the performance profile of the previously databased application, the load data at the next time, and the system load measured at the current time, and selects a video at the current time. A mode that is determined to be most efficient for processing can be selected.

In this case, a mode for processing a video may be selected by comparing system resource efficiency when the video is processed in the software mode and system resource efficiency when the video is processed in the hardware mode.

In this case, the system resource efficiency may be determined by considering the occupancy rate of each of the software resource and the hardware resource or the load degree of each of the software resource and the hardware resource.

For example, assuming that software resource occupancy and hardware resource occupancy can be measured from 0 to 100, respectively, the more similar the software resource occupancy value and the hardware resource occupancy value are, the higher the system resource efficiency can be determined.

For another example, assuming that software resource and hardware resource loads can be measured from 0 to 100, respectively, it can be determined that system resource efficiency is higher as the load of software resources and the load of hardware resources are similar.

As a result of the determination in step S614, if the software mode is selected as the mode for processing the current video, the video can be decoded and rendered in the software mode (S616) and streamed to the user's terminal (S620).

In addition, as a result of the determination in step S614, if the hardware mode is selected as the mode for processing the current video, the video can be decoded and rendered in the hardware mode (S618) and streamed to the user's terminal (S620).

By providing a cloud streaming service through a cloud streaming service method based on selective video processing in consideration of such resources and loads, processing is performed to provide a service by selectively using software and hardware resources supported by the cloud streaming service system. By doing this, the resources in the system can be used to the maximum limit.

In addition, processing for service provision (decoding and rendering, etc.) can be performed more efficiently by considering the load of the next time that is predicted by considering the performance profile of the application serviced through the cloud streaming server.

In addition, by efficiently using resources within the cloud streaming service system to the maximum limit, it is possible to provide services to more users by increasing the number of concurrent users to the cloud streaming server.

7 is an operational flowchart showing in detail a process of selecting a mode of the cloud streaming service method according to the present invention.

Referring to FIG. 7, in the process of selecting a mode among the cloud streaming service methods according to the invention, first, after the application is executed, when the performance profile, the next time load data, and the system load are all input (S710), the video is played in software mode. System resource efficiency in the case of processing and system resource efficiency in the case of processing the video in hardware mode can be calculated respectively (S720 and 730).

In this case, the system resource efficiency may be determined by considering the occupancy rate of each of the software resource and the hardware resource or the load degree of each of the software resource and the hardware resource.

For example, assuming that software resource occupancy and hardware resource occupancy can be measured from 0 to 100, respectively, the more similar the software resource occupancy value and the hardware resource occupancy value are, the higher the system resource efficiency can be determined.

For another example, assuming that software resource and hardware resource loads can be measured from 0 to 100, respectively, it can be determined that system resource efficiency is higher as the load of software resources and the load of hardware resources are similar.

Thereafter, it is determined whether the system resource efficiency when the video is processed in the software mode is higher than the system resource efficiency when the video is processed in the hardware mode (S735), and the system resource efficiency when the video is processed in the software mode. If the hardware mode is higher than system resource efficiency when the video is processed, the software mode may be selected as a mode to process the video (S740).

In addition, as a result of the determination in step S735, if the efficiency of system resources in the case of processing the video in the software mode is not higher than that in the case of processing the video in the hardware mode, the hardware mode can be selected as the mode to process the video. (S750).

Thereafter, the video can be processed through the selected mode and then streamed to the user's terminal.

8 is a diagram illustrating a cloud streaming server based on selective video processing considering resources and loads according to an embodiment of the present invention.

Referring to Figure 8, the cloud streaming server according to an embodiment of the present invention may be implemented in a computer system such as a computer-readable recording medium. As shown in FIG. 8 , computer system 800 includes one or more processors 810, memory 830, user input devices 840, user output devices 850, and storage that communicate with each other via a bus 820. (860). In addition, computer system 800 may further include a network interface 870 coupled to network 880 . The processor 810 may be a central processing unit or a semiconductor device that executes processing instructions stored in the memory 830 or the storage 860 . Memory 830 and storage 860 may be various types of volatile or non-volatile storage media. For example, the memory may include ROM 831 or RAM 832 .

Accordingly, embodiments of the present invention may be implemented as a computer-implemented method or a non-transitory computer-readable medium in which instructions executable by the computer are recorded. When executed by a processor, the computer readable instructions may perform a method according to at least one aspect of the present invention.

The processor 810 generates a performance profile of the application based on the load test.

For example, in the cloud streaming server according to an embodiment of the present invention, a performance profile for at least one application executable in the cloud streaming server may be generated through the performance profile generating unit 310 shown in FIG. 3 .

In this case, the performance profile may correspond to data obtained by measuring load for each time point of the application in a software mode and a hardware mode, respectively.

At this time, the software mode may correspond to a mode for decoding and rendering video or content by utilizing software resources such as GPU of the cloud streaming service system, and the hardware mode utilizes hardware resources such as CPU of the cloud streaming service system. This may correspond to a mode for decoding and rendering video or content.

Therefore, in the present invention, in order to perform efficient processing in consideration of resources and loads, data on loads at each point in time of applications for each of software resources and hardware resources may be stored as a database.

For example, a performance profile of an application may be created and stored as a database as shown in FIG. 4 .

At this time, each APP viewpoint shown in FIG. 4 may correspond to a viewpoint corresponding to an application execution screen that is sequentially displayed when an application is executed.

For example, it can be assumed that the time point corresponding to the first execution screen displayed after the application is executed is APP time #1, and APP time #2 or APP time depending on the input generated from the user terminal at APP time #1. The viewpoint can be changed to the execution screen corresponding to #3.

Therefore, in the present invention, the process of decoding, rendering, and streaming the application execution screen for display to the user at each point in time of the application is calculated by calculating the load when processing the software resource and the hardware resource, respectively, and generating the performance profile of the application. can do.

At this time, the performance profile of the application shown in FIG. 4 is an example, and may be created and stored in a database in various forms other than the form shown in FIG. 4 .

In addition, the processor 810 predicts the behavior of the application at the next time and generates next time load data corresponding to the application.

In this case, the next load data may include information about a software mode load corresponding to a behavior at a next time point and a hardware mode load corresponding to a behavior at a next time point among loads for each time point.

For example, referring to FIG. 4 , if it is assumed that the current time point for an application is APP time #2, the possible next time points are APP time #4, APP time #5, and APP time #6. may apply.

At this time, in the present invention, the application behavior prediction unit 320 shown in FIG. 3 calculates statistics on the point in time at which the application moves after a specific point in time while providing the cloud streaming service, and stores it in a database together with the performance profile. And, using the databased statistics, it is possible to predict the next action.

For example, whenever an application execution is requested from the user, the next point in time shown in FIG. 4 is accumulated and recorded, and based on the accumulated record, a statistical ratio corresponding to the action at the next point in time is calculated. can be calculated

If the behavior of the next time is predicted at APP time #3, the statistic moving from APP time #3 to APP time #7 is compared with the statistic moving from APP time #3 to APP time #8 to obtain a higher percentage of the statistic. The time of having can be predicted as the next time action. Alternatively, when predicting the next action at APP time #4, APP time #9 can be predicted as the next action since there is only one next time point that can be moved from APP time #4.

If the behavior at the next time point is predicted in this way, the next time load data generating unit 330 shown in FIG. 3 processes the predicted next time behavior and the load degree when the predicted next time behavior is processed in the software mode and the predicted next time behavior in the hardware mode. Load data at the next point in time can be created by considering the load degree in the case of

In this case, the next time load data may correspond to data obtained by adding the software mode load and the hardware mode load for the predicted next time behavior to the respective load levels of the software resource and the hardware resource at the current time point.

In this way, by adding the load for the next action to the load at the current time and then using the load data at the time, the selection of a processing mode that can cause overflow due to the action at the next time can be avoided or a more generous processing mode is selected. can be scheduled.

In addition, the processor 810 selects a mode (MODE) for processing a video corresponding to the current view in consideration of the performance profile, the load data of the next view, and the system load of the current view.

In this case, the mode may correspond to any one of a software mode and a hardware mode.

In this case, the software mode may operate based on the GPU, and the hardware mode may operate based on the CPU.

For example, in the present invention, the mode selector 340 shown in FIG. 3 complexly considers the performance profile of the previously databased application, the load data at the next time, and the system load measured at the current time, and selects a video at the current time. A mode that is determined to be most efficient for processing can be selected.

In this case, a mode for processing a video may be selected by comparing system resource efficiency when the video is processed in the software mode and system resource efficiency when the video is processed in the hardware mode.

In this case, the system resource efficiency may be determined by considering the occupancy rate of each of the software resource and the hardware resource or the load degree of each of the software resource and the hardware resource.

For example, assuming that software resource occupancy and hardware resource occupancy can be measured from 0 to 100, respectively, the more similar the software resource occupancy value and the hardware resource occupancy value are, the higher the system resource efficiency can be determined.

For another example, assuming that software resource and hardware resource loads can be measured from 0 to 100, respectively, it can be determined that system resource efficiency is higher as the load of software resources and the load of hardware resources are similar.

In addition, the processor 810 processes the video in the selected mode and streams it to the user's terminal.

For example, a video may be processed in a mode selected by the streaming unit 350 shown in FIG. 3 and streamed to the user's terminal.

At this time, the user's terminal may receive an application execution screen corresponding to the cloud streaming service from the cloud streaming server, that is, a video processing screen, and provide it to the user.

In addition, the user's terminal is a device that is connected to a communication network and can run applications based on a cloud computing system, and is not limited to a mobile communication terminal, and is not limited to all information communication devices, multimedia terminals, wired terminals, fixed terminals, and IP (Internet Protocol) It may be various terminals, such as a terminal. In addition, the user's terminal is a mobile phone, PMP (Portable Multimedia Played), MID (Mobile Internet Device), smart phone (Smart Phone), desktop (Desktop), tablet computer (Tablet PC), notebook (Note book), netbook (Netbook) Book), a personal digital assistant (PDA), a smart TV, and a mobile terminal having various mobile communication specifications, such as an information communication device.

In addition, the user's terminal can receive various information such as numbers and text information, set various functions, and transmit input signals related to function control of the user's terminal to the control unit through the input unit. In addition, the input unit of the user's terminal may include at least one of a keypad and a touchpad that generate an input signal according to a user's touch or manipulation. At this time, the input unit of the user's terminal is configured in the form of a single touch panel (or touch screen) together with the display unit of the user's terminal to simultaneously perform input and display functions. In addition, all types of input means that may be developed in the future may be used for the input unit of the user's terminal, in addition to input devices such as a keyboard, keypad, mouse, joystick, and the like. In particular, the input unit of the user's terminal according to the present invention may transmit an input signal for uploading or downloading content based on cloud computing to the control unit of the user's terminal.

In addition, the display unit of the user's terminal may display information about a series of operation states and operation results occurring while the user's terminal performs functions. Also, the display unit of the user's terminal may display a menu of the user's terminal and user data input by the user. Here, the display unit of the user's terminal includes a liquid crystal display (LCD), a thin film transistor LCD (TFT-LCD), a light emitting diode (LED), an organic light emitting diode (OLED, Organic LED), active matrix OLED (AMOLED), Retina Display, flexible display, and 3D display. In this case, when the display unit of the user terminal is configured in the form of a touch screen, the display unit of the user terminal may perform some or all of the functions of the input unit of the user terminal. In particular, the display unit of the user's terminal according to the present invention may display information related to the execution of content provided based on cloud computing on the screen.

Also, the storage unit of the user's terminal is a device for storing data, includes a main storage device and an auxiliary storage device, and may store application programs required for functional operation of the user's terminal. The storage unit of the user's terminal may largely include a program area and a data area. Here, when the user's terminal activates each function in response to the user's request, the corresponding application programs are executed under the control of the control unit to provide each function. In particular, the storage unit of the user's terminal according to the present invention may store an operating system for booting the user's terminal, a program for uploading or downloading content based on cloud computing, and the like. In addition, the storage unit of the user's terminal may store a content DB for storing a plurality of contents and information of the user's terminal. In this case, the content DB includes execution data for executing the content and attribute information about the content, and may store content use information according to content execution. And, the user's terminal information may include terminal specification information.

In addition, the communication unit of the user's terminal may perform a function for transmitting and receiving data through a cloud streaming server and a network. Here, the communication unit of the user's terminal may include an RF transmitting means for up-converting and amplifying the frequency of a transmitted signal, and an RF receiving means for low-noise amplifying a received signal and down-converting the frequency. The communication unit of the user's terminal may include at least one of a wireless communication module and a wired communication module. And, the wireless communication module is a configuration for transmitting and receiving data according to a wireless communication method, and when the user's terminal uses wireless communication, data is transmitted using any one of a wireless network communication module, a wireless LAN communication module, and a wireless fan communication module. can be transmitted and received to the cloud streaming server. In addition, the wired communication module is for transmitting and receiving data by wire. The wired communication module may access a network through a wire and transmit/receive data to a cloud streaming server. That is, the user's terminal accesses the network using a wireless communication module or a wired communication module, and can transmit and receive data to and from the cloud streaming server through the network. In particular, the network according to the present invention can communicate with a cloud streaming server or another user's terminal to transmit and receive data necessary for uploading or downloading content based on cloud computing.

In addition, the control unit of the user's terminal may be an operating system (OS) and a process device that drives each component. For example, the controller may control the entire process of accessing the cloud streaming server. When accessing the cloud streaming server through a separate service application, it is possible to control the entire process of running the service application according to the user's request, and control the service use request to be transmitted to the cloud streaming server at the same time as the execution, At this time, control may be performed so that information of the user's terminal required for user authentication is transmitted together.

Also, the controller of the user's terminal may execute specific content stored in the storage of the user's terminal according to the user's request. In this case, the controller may store a content use history according to content execution as content use information.

In addition, the control unit of the user's terminal can transmit and upload execution data for executing the content, content information including attribute information about the content, and content use information, which is information according to the content use history, to the cloud streaming server. there is. Thereafter, the control unit may delete the content uploaded to the cloud streaming server from the storage unit of the user's terminal according to the user's request, and may connect to the cloud streaming server to execute and use the content through the cloud streaming server.

In addition, the control unit of the user's terminal may access the cloud streaming server to download content from another user's terminal and then control the storage to be stored. When the content is executed through the cloud streaming server, only data necessary for execution is received. You can also control the content to be executed later.

In this case, the mode may be changed from the software mode to the hardware mode or from the hardware mode to the software mode according to system resource efficiency.

For example, in the present invention, the degree of occupancy or load of software resources and hardware resources is measured for each predetermined measurement period while the cloud streaming service is continuously provided, and if the occupancy or load is excessively high in either direction, The processing mode of the currently provided service may be changed to another processing mode.

Memory 830 stores performance profile and next time load data.

In addition, the memory 830 stores various information generated in the cloud streaming server according to an embodiment of the present invention as described above.

Depending on the embodiment, the memory 830 may be configured independently of the cloud streaming server to support functions for the cloud streaming service. At this time, the memory 830 may operate as a separate mass storage and may include a control function for performing an operation.

On the other hand, the cloud streaming server is equipped with a memory can store information in the device. In one implementation, the memory is a computer readable medium. In one implementation, the memory may be a volatile memory unit, and in another implementation, the memory may be a non-volatile memory unit. In one implementation, the storage device is a computer readable medium. In various different implementations, the storage device may include, for example, a hard disk device, an optical disk device, or some other mass storage device.

By providing cloud streaming services through a cloud streaming server based on selective video processing in consideration of such resources and loads, by selectively using software and hardware resources supported by the cloud streaming service system to perform processing to provide services. The resources in the system can be used to the maximum limit.

In addition, processing for service provision (decoding and rendering, etc.) can be performed more efficiently by considering the load of the next time that is predicted by considering the performance profile of the application serviced through the cloud streaming server.

In addition, by efficiently using resources within the cloud streaming service system to the maximum limit, it is possible to provide services to more users by increasing the number of concurrent users to the cloud streaming server.

Implementations of the functional operations and subject matter described herein may be implemented in digital electronic circuitry, or in computer software, firmware, or hardware, including the structures disclosed herein and their structural equivalents, or in a combination of one or more of the foregoing. can be implemented Implementations of the subject matter described herein are one or more computer program products, that is, one or more modules of computer program instructions encoded on a tangible program storage medium for execution by or for controlling the operation of a processing system. can be implemented

A computer readable medium may be a machine readable storage device, a machine readable storage substrate, a memory device, a composition of matter that affects a machine readable propagating signal, or a combination of one or more of these.

In this specification, the term 'system' or 'apparatus' encompasses all devices, devices, and machines for processing data, including, for example, a programmable processor, computer, or multiple processors or computers. A processing system may include, in addition to hardware, code that forms an execution environment for computer programs on demand, such as, for example, code constituting processor firmware, a protocol stack, a database management system, an operating system, or a combination of one or more of these. .

A computer program (also known as a program, software, software application, script or code) may be written in any form of programming language, including compiled or interpreted language, a priori or procedural language, and may be a stand-alone program or module; It may be deployed in any form including components, subroutines, or other units suitable for use in a computer environment. A computer program does not necessarily correspond to a file in a file system. A program may be in a single file provided to the requested program, or in multiple interacting files (e.g., one or more modules, subprograms, or files that store portions of code), or parts of files that hold other programs or data. (eg, one or more scripts stored within a markup language document). A computer program may be deployed to be executed on a single computer or multiple computers located at one site or distributed across multiple sites and interconnected by a communication network.

On the other hand, computer-readable media suitable for storing computer program instructions and data include, for example, semiconductor memory devices such as EPROM, EEPROM and flash memory devices, magnetic disks such as internal hard disks or external disks, magneto-optical disks and CDs. - may include all forms of non-volatile memory, media and memory devices, including ROM and DVD-ROM disks. The processor and memory may be supplemented by, or incorporated into, special purpose logic circuitry.

Implementations of the subject matter described herein may include back-end components, such as, for example, data servers, or may include middleware components, such as, for example, application servers, or may include, for example, web browsers or graphical users through which users may interact with implementations of the subject matter described herein. It may also be implemented in a computing system that includes a front-end component, such as a client computer having an interface, or any combination of one or more of such back-ends, middleware, or front-end components. The components of the system are interconnectable by any form or medium of digital data communication, such as, for example, a communication network.

Although this specification contains many specific implementation details, they should not be construed as limiting on the scope of any invention or what is claimed, but rather as a description of features that may be unique to a particular embodiment of a particular invention. It should be understood. Likewise, certain features that are described in this specification in the context of separate embodiments may also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments individually or in any suitable subcombination. Further, while features may operate in particular combinations and are initially depicted as such claimed, one or more features from a claimed combination may in some cases be excluded from that combination, and the claimed combination is a subcombination. or sub-combination variations.

In addition, although operations are depicted in the drawings in a specific order in this specification, it is not to be understood that such operations must be performed in the specific order shown or sequential order or that all illustrated operations must be performed in order to obtain desired results. Can not be done. In certain cases, multitasking and parallel processing can be advantageous. Further, the separation of various system components in the embodiments described above should not be understood as requiring such separation in all embodiments, and the program components and systems described may generally be integrated together into a single software product or packaged into multiple software products. It should be understood that you can

As such, this specification is not intended to limit the invention to the specific terms presented. Therefore, although the present invention has been described in detail with reference to the above-described examples, those skilled in the art may make alterations, changes, and modifications to the present examples without departing from the scope of the present invention. The scope of the present invention is indicated by the following claims rather than the detailed description above, and all changes or modifications derived from the meaning and scope of the claims and their equivalent concepts should be interpreted as being included in the scope of the present invention. do.

According to the present invention, a performance profile of an application is created based on a load test, the behavior of the next time for the application is predicted, load data at the next time corresponding to the application is generated, the performance profile, the load data at the next time, and the current system Considering the load, a mode for processing a video corresponding to a current point of view is selected, and the video is processed in the selected mode and streamed to the user's terminal. Furthermore, by selectively using software and hardware resources supported by the cloud streaming service system to perform processing to provide the service, resources within the system can be used to the maximum limit, thereby reducing the number of concurrent users to the cloud streaming server. You can operate the cloud streaming system more efficiently by increasing

110: cloud streaming server 120-1 to 120-N: terminal device
130: network 310: performance profile generator
320: application behavior prediction unit 330: next time load data generation unit
340: mode selection unit 350: streaming unit
510: Performance Profile 520: Next Point Load Data
530: system load 800: computer system
810: processor 820: bus
830: memory 831: ROM
832: RAM 840: User input device
850: user output device 860: storage
870: network interface 880: network

Claims (10)

A performance profile of the application is generated based on the load test, next-time behavior of the application is predicted, and next-time load data corresponding to the application is generated, and the performance profile, the next-time load data, and the current system a processor for selecting a mode for processing a video corresponding to a current view in consideration of a load, processing the video in the selected mode, and streaming the video to a user's terminal; and
Memory for storing the performance profile and the load data at the next point in time
Cloud streaming server comprising a. The method of claim 1,
The mode
Cloud streaming server, characterized in that corresponding to any one of the software mode and hardware mode. The method of claim 2,
The performance profile is
Cloud streaming server, characterized in that corresponding to the data measured at each time load for the application in the software mode and the hardware mode, respectively. The method of claim 3,
The load data at the next point in time
Cloud streaming server, characterized in that it comprises information about the hardware mode load corresponding to the software mode load and the next time action corresponding to the next time action of the load for each time point. The method of claim 2,
The processor
Cloud streaming server, characterized in that for selecting a mode for processing the video by comparing system resource efficiency when the video is processed in the software mode and system resource efficiency when the video is processed in the hardware mode. The method of claim 5,
The mode
Cloud streaming server, characterized in that it is possible to change from the software mode to the hardware mode, or from the hardware mode to the software mode according to the system resource efficiency. The method of claim 2,
The software mode operates based on a GPU, and the hardware mode is a cloud streaming server, characterized in that it operates based on a CPU. generating a performance profile of the application based on the load test;
generating next-time load data corresponding to the application by predicting next-time behavior for the application;
selecting a mode for processing a video corresponding to a current view in consideration of the performance profile, the load data of the next view, and the system load of the current view; and
Processing the video in the selected mode and streaming it to the user's terminal
Cloud streaming service method comprising a. The method of claim 8,
The mode
Cloud streaming service method, characterized in that corresponding to any one of the software mode and hardware mode. The method of claim 9,
The performance profile is
Cloud streaming service method, characterized in that the load for each time point for the application corresponds to the data measured in the software mode and the hardware mode, respectively.

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