CN114004979B - High-cost performance data storage method and system in cloud rendering - Google Patents
High-cost performance data storage method and system in cloud rendering Download PDFInfo
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Abstract
The application discloses a high cost performance data storage method and a high cost performance data storage system in cloud rendering, wherein the method comprises the steps of obtaining cloud rendering data to be stored; setting the use frequency of planning resources based on the application scene of the data; extracting characteristic elements and basic overturning heat alpha of data; setting a theoretical resource access frequency according to the characteristic elements; obtaining the actual resource access frequency; calculating data access heat F; comparing F with alpha, if F > alpha, switching the storage space from large space to small space, otherwise, switching the storage space from small space to large space. According to the application, the three-frequency resonance is set to be the optimal resource use storage mode aiming at different service rhythms and data access frequencies, cold and hot data are stored separately, and the storage space is adjusted in real time for the cold and hot ice data, so that the technical effects of releasing the local storage space and reducing the cost are achieved.
Description
Technical Field
The application relates to a high-cost performance data storage method and system in cloud rendering.
Background
The cloud desktop can provide strong GPU computing power and elastic storage resource support for the bottom layer in the post-rendering process in the digital content production of animation films, film and television special effects, construction visualization and the like. In terms of project cost management, factors influencing cloud rendering cost mainly comprise computing power, storage resources and the like, wherein the storage cost accounts for more than half, and therefore, control and management of the cloud storage cost are key problems.
In the industry, according to the business rhythm and the data access heat, the stored data are divided into three types, namely hot stored data, cold stored data and ice stored data, and different stored data have different characteristics, and from the aspect of access frequency, the hot storage is most frequent, the cold storage is inferior, and the ice storage is almost not accessed; from the storage period, the ice storage period is longest, and the cold storage period and the hot storage period are shortest; and from the perspective of storage space, the memory occupation of the ice storage data is maximum, and the storage data between the hot storage and the cold storage are overlapped, so that flexible scheduling and flowing can be realized.
The existing cloud desktop data storage during rendering adopts a distributed storage system, a plurality of servers are utilized to share storage load through a transversely expanded multi-level multi-node standardized storage space, and a position server is adopted to position storage information, so that cluster expansion can be performed according to the rapidly increased data volume; however, the storage mode adopts a single storage strategy aiming at different business rhythms, so that the storage cost is difficult to effectively control, the storage mode is a waste of cluster resources, and the storage cost is increased.
However, in the process of implementing the technical scheme of the embodiment of the application, the inventor discovers that the above technology has at least the following technical problems:
the storage mode adopts a single storage strategy aiming at different business rhythms, so that the storage cost is difficult to effectively control, the storage mode is a waste of cluster resources, and the storage cost is increased.
However, according to different storage data characteristics, if the adopted storage schemes are different, the storage cost is different, the gap is large, and the different storage costs are multiplied.
Therefore, there is a need for a cost-effective data storage method in cloud rendering.
Disclosure of Invention
The embodiment of the application solves the technical problem of high storage cost in the prior art by providing the high-cost performance data storage method in cloud rendering, and achieves the technical effects of releasing the local storage space and reducing the cost.
The present application has been made in view of the above problems, and it is an object of the present application to provide a device that overcomes or at least partially solves the above problems.
In a first aspect, an embodiment of the present application provides a cost performance data storage method in cloud rendering, where the method includes:
acquiring cloud rendering data to be stored;
setting the use frequency omega of planning resources based on application scenes of data 1 ；
Extracting characteristic elements and basic overturning heat alpha of data;
setting theoretical resource access frequency omega according to characteristic elements 2 ；
Obtaining the actual resource access frequency omega;
the data access heat F is calculated and,where K is the frequency, ω ', of the data' 1 ＝ω 1 modΩ，ω′ 2 ＝ω 2 modΩ；
Comparing F with alpha, if F > alpha, switching the storage space from large space to small space, otherwise, switching the storage space from small space to large space.
Further, wherein the storage space includes:
ice space, cold space, and hot space; the spatial size relationship of the three spaces is as follows: ice space > cold space > hot space.
Further, the obtaining cloud rendering data to be stored includes:
classifying cloud rendering data to be stored into five types of data: model data, trajectory data, evolution data, composition data, and interface data; where K is the frequency of classification data, k=k 1 k 2 k 3 k 4 k 5 The method comprises the steps of carrying out a first treatment on the surface of the The feature elements of the extracted data comprise associated feature elements extracted and stored according to preset relevance.
Further, wherein the calculating the data access heat F includes:
judging whether the three-frequency resonance standard is reached or not according to the calculated data access heat F value; if the three-frequency resonance standard is not met, performing feature element iterative optimization, and resetting the planning resource use frequency omega 1 And theoretical resource access frequency omega 2 Until the three-frequency resonance standard is reached;
the judgment standard of the three-frequency resonance is as follows: K/F is more than or equal to 0.9 and less than or equal to 1.
Further, comparing F with α, if F > α, switching the storage space from the large space to the small space, otherwise, switching the storage space from the small space to the large space, and then further includes:
and analyzing the user by adopting a mode priority principle, and optimizing a storage scheme according to the requirements of the user on the storage price and performance.
Further, in this case, among others,
when data is stored in the ice space, the data is classified and compressed.
Further, the cold space is divided into a cold storage space and a refrigerating space, and the space size relationship between the cold storage space and the refrigerating space is as follows: the refrigerating space is larger than the cold storage space.
On the other hand, the application also provides a cost performance data storage system in cloud rendering, wherein the system comprises:
the cloud rendering device comprises a first obtaining unit, a second obtaining unit and a storage unit, wherein the first obtaining unit is used for obtaining cloud rendering data to be stored;
a first setting unit for setting a planned resource usage frequency ω based on an application scenario of the data 1 ；
A first extraction unit for extracting feature elements and a basic flip heat α of data;
a second setting unit for setting a theoretical resource access frequency omega based on the feature elements 2 ；
A second obtaining unit, configured to obtain an actual resource access frequency Ω;
a first calculation unit for calculating a data access heat F,where K is the frequency, ω ', of the data' 1 ＝ω 1 modΩ，ω′ 2 ＝ω 2 modΩ；
A first comparing unit for comparing F with α;
and the first execution unit is used for switching the storage space from the large space to the small space if F & gtalpha after the first comparison unit judges, otherwise, switching the storage space from the small space to the large space.
In a third aspect, an embodiment of the present application provides an electronic device, including a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and capable of running on the processor, where the transceiver, the memory, and the processor are connected by the bus, and the computer program when executed by the processor implements the steps in the method for storing cost performance data in cloud rendering according to any one of the above.
In a fourth aspect, an embodiment of the present application further provides a computer readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps in the foregoing cost-effective data storage method in the mid-cloud rendering.
One or more technical solutions provided in the embodiments of the present application at least have the following technical effects or advantages:
the application provides a high cost performance data storage method in cloud rendering for a cloud desktop, and aims at different business rhythms and data access frequencies, a storage mode which achieves three-frequency resonance as an optimal resource use is set, cold and hot data are stored separately, and storage space is adjusted for the cold and hot ice data in real time, so that the technical effects of releasing local storage space and reducing cost are achieved.
The foregoing description is only an overview of the present application, and is intended to be implemented in accordance with the teachings of the present application in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present application more readily apparent.
Drawings
FIG. 1 is a flow chart of a cost-effective data storage and transmission method in cloud rendering according to an embodiment of the application;
fig. 2 is a schematic diagram of classification data according to an embodiment of the application.
Fig. 3 is a schematic diagram of monitoring and obtaining actual resource access frequency in an embodiment of the present application.
FIG. 4 is a schematic diagram of various data transfer in a cold and hot ice space according to an embodiment of the present application.
FIG. 5 is a schematic diagram of a cost-effective data storage control system in cloud rendering according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of an electronic device for performing a method for controlling output data according to an embodiment of the present application.
Reference numerals illustrate: the device comprises a first obtaining unit 11, a first setting unit 12, a first extracting unit 13, a second setting unit 14, a second obtaining unit 15, a first calculating unit 16, a first comparing unit 17, a bus 1110, a processor 1120, a transceiver 1130, a bus interface 1140, a memory 1150 and a user interface 1160.
Detailed Description
In the description of the embodiments of the present application, those skilled in the art will appreciate that the embodiments of the present application may be implemented as a method, an apparatus, an electronic device, and a computer-readable storage medium. Thus, embodiments of the present application may be embodied in the following forms: complete hardware, complete software (including firmware, resident software, micro-code, etc.), a combination of hardware and software. Furthermore, in some embodiments, embodiments of the application may also be implemented in the form of a computer program product in one or more computer-readable storage media having computer program code embodied therein.
Any combination of one or more computer-readable storage media may be employed by the computer-readable storage media described above. The computer-readable storage medium includes: an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of the computer readable storage medium include the following: portable computer magnetic disks, hard disks, random access memories, read-only memories, erasable programmable read-only memories, flash memories, optical fibers, optical disk read-only memories, optical storage devices, magnetic storage devices, or any combination thereof. In embodiments of the present application, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, device.
Summary of the application
The embodiment of the application describes a method, a device and electronic equipment through flowcharts and/or block diagrams.
It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions. These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.
These computer readable program instructions may also be stored in a computer readable storage medium that can cause a computer or other programmable data processing apparatus to function in a particular manner. Thus, instructions stored in a computer-readable storage medium produce an instruction means which implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.
Embodiments of the present application will be described below with reference to the accompanying drawings in the embodiments of the present application.
Example 1
As shown in fig. 1, an embodiment of the present application provides a cost-effective data storage method in cloud rendering, where the method includes:
step S100, cloud rendering data to be stored is obtained;
step S200, setting the use frequency omega of planning resources based on the application scene of the data 1 ；
Step S300, extracting characteristic elements and basic overturning heat alpha of data;
in step S400 of the process of the present application,setting theoretical resource access frequency omega according to characteristic elements 2 ；
Step S500, obtaining the actual resource access frequency omega;
step S600, calculating data access heat F,where K is data, ω' 1 ＝ω 1 modΩ，ω′ 2 ＝ω 2 modΩ；
Step S700, comparing F with alpha, if F > alpha, switching the storage space from large space to small space, otherwise, switching the storage space from small space to large space.
In step S100, after the cloud rendering data to be stored is obtained, the cloud rendering data to be stored is classified into five types of data as shown in fig. 2: model data, trajectory data, evolution data, composition data, and interface data; where K is the frequency of classification data, k=k 1 k 2 k 3 k 4 k 5 In the following steps S200-S700, the related data refers to classified data. In particular, the model data is a rendering base data set; the track data is external assets and a local data set used in track scene rendering; the evolution data is a data set obtained by transforming information such as coloring, light rays and the like; the combined data is a data set obtained by superposing track data and evolution data; the interface data is the interface rendering dataset that interfaces with the application.
In step S200, the application scenario (such as animation film, movie effect, and construction visualization) of the classified data is analyzed, the user storage requirement and price requirement (i.e. which files need to be stored, which files have storage requirements and cost to be paid) are comprehensively considered, and the planning resource usage frequency ω is set under each business progress 1 . Overall, planning resource usage frequency favors overall planning from a phase (time) perspective of the job, such as how many spatial resources are each given in each phase of modeling/tiling/rendering, how to store.
In step S300, a trace-cause false approach is adopted, a storage policy and a solution are formulated by making assumptions and data-based decisions, feature elements of each classified data are extracted, and a basic flip heat α in the classified data is extracted by analysis to serve as a basic storage space switching threshold. The feature elements of the extracted data include extracting and storing associated feature elements according to preset relevance, and considering that the feature elements of the internal classification data are numerous, if all the feature elements are extracted and stored, a lot of resource space is occupied, so that it can be preset which feature elements need to be extracted and stored, and only the relevant feature elements, such as storage resources, resource types and the like, can be extracted and stored. The basic overturning heat alpha is formed gradually, a fixed basic value is firstly established, then overturning test analysis is carried out, and the basic overturning heat alpha is continuously optimized, so that the stable overturning heat with lower jumping cost is obtained.
In step S400, a theoretical resource access frequency ω is set based on the feature elements 2 . Specifically, theoretical resource access frequency ω 2 Is the planning resource use frequency omega 1 More specifically how resources are allocated to each link.
In step S500, in the rendering workflow, information of a storage object, a storage period, a storage method, a benefit correlation, an entity, and the like is monitored according to fig. 3 and recorded in real time, so as to obtain an actual resource access frequency Ω. Specifically, the storage object includes classification data and feature elements of each classification data; is the storage period included short term? Is the middle term? Is it stored for a long period? Storage methods include qualitative (type of data stored) and quantitative (required storage space); benefit-related refers to internal data or external data; the entity refers to the user's data or the system's own data.
In step S600, a storage model is constructed according to the feature elements, the actual resource access frequency data actually monitored is compared with the planned resource use frequency and the theoretical resource access frequency, the data access heat F is calculated,where K is data, ω' 1 ＝ω 1 modΩ，ω′ 2 ＝ω 2 mod Ω. As can be seen from the calculation formula, if the actual resource access frequency data and the planned resource use frequency and the actual resource access frequency and the theoretical resource access frequency are closer to each other, the remainder is smaller, and the F and K are closer to each other. Judging whether the three-frequency resonance standard is reached or not according to the calculated data access heat F value; the judgment standard of the three-frequency resonance is as follows: K/F is more than or equal to 0.9 and less than or equal to 1; if the three-frequency resonance standard is not met, performing feature element iterative optimization, and resetting the planning resource use frequency omega 1 And theoretical resource access frequency omega 2 Until the three-frequency resonance standard is reached.
In step S700, F is compared with α, and if F > α, the storage space is switched from large space to small space, otherwise, the storage space is switched from small space to large space. Specifically, when the access heat is changed from low to high, that is, the classified data access heat F is greater than the basic flip heat α (F > α), the storage space of the classified data is changed from L i →L j Switching (where i > j); conversely, when the access heat is changed from high to low, F < alpha, the storage space of the classified data is changed from L i To L j Switching (where i < j) to reduce the storage cost of the toggle generation. Wherein, the memory space includes: ice space, cold space, and hot space; the spatial size relationship of the three spaces is as follows: ice space > cold space > hot space. Dividing the cold space into a cold storage space and a refrigerating space, wherein the space size relationship between the cold storage space and the refrigerating space is as follows: the refrigerating space is larger than the cold storage space. Referring specifically to fig. 4, various phases and wakefulness of various data jumps in ice, cold, hot spaces are shown. In this figure, the storage space is changed from large to small, the storage time is changed from long to short, and the storage cost is changed from small to large. Further, when data is stored in the ice space, the data is classified and compressed to further reduce storage costs. And after the data in the cold storage reach a preset standard, the data are placed in the cold storage area, and then the data reach a certain standard and are compressed into the ice storage area.
Further, the solution of S700 may be used as a preliminary storage solution, further adopting a mode priority principle to analyze the user, and optimizing the storage solution according to the requirements of the user on the storage price and performance.
In summary, the method and the system for storing cost performance data in cloud rendering provided by the embodiment of the application have the following technical effects: according to different business rhythms and data access frequencies, a storage mode for achieving optimal resource use by achieving three-frequency resonance is set, cold and hot data are stored separately, and storage space is adjusted in real time for the cold and hot ice data, so that the technical effects of releasing local storage space and reducing cost are achieved.
Example two
Based on the same inventive concept as the cost performance data storage method in cloud rendering in the foregoing embodiment, the present application further provides a cost performance data storage system in cloud rendering, as shown in fig. 5, where the system includes:
a first obtaining unit 11 for obtaining cloud rendering data to be stored;
a first setting unit 12 for setting a planned resource usage frequency ω based on an application scenario of the data 1 ；
A first extraction unit 13 for extracting a feature element of data and a basic flip heat α;
a second setting unit 14 for setting a theoretical resource access frequency ω based on the feature elements 2 ；
A second obtaining unit 15 for obtaining an actual resource access frequency Ω;
a first calculation unit 16 for calculating a data access heat F,where K is the frequency, ω ', of the data' 1 ＝ω 1 modΩ，ω′ 2 ＝ω 2 modΩ)
A first comparing unit 17 for comparing F with α;
the first comparing unit further comprises a first executing unit, and the first executing unit is used for switching the storage space from the large space to the small space if F & gtalpha after the first comparing unit judges that F & gtalpha, otherwise, switching the storage space from the small space to the large space.
Further, the first obtaining unit 11 further includes a first classifying unit, configured to classify cloud rendering data to be stored.
Further, a first division unit for dividing the storage space into an ice space, a cold space and a hot space is further included.
Further, the first calculating unit 16 further includes a first judging unit for judging whether the triple-frequency resonance is reached.
Further, the method further includes a first compression unit, configured to compress data stored in the ice space, and various modifications and specific examples of the method for storing cost performance data in cloud rendering in the first embodiment of fig. 1 are equally applicable to the system for storing cost performance data in cloud rendering in this embodiment, and by the foregoing detailed description of the method for storing cost performance data in cloud rendering, those skilled in the art can clearly know the implementation method of the system for storing cost performance data in cloud rendering in this embodiment, so that, for brevity of description, details will not be given here.
In addition, the embodiment of the application also provides an electronic device, which comprises a bus, a transceiver, a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the transceiver, the memory and the processor are respectively connected through the bus, and when the computer program is executed by the processor, the processes of the embodiment of the cost performance data storage method in cloud rendering can be realized, and the same technical effect can be achieved, so that repetition is avoided and redundant description is omitted.
Exemplary electronic device
In particular, referring to FIG. 6, an embodiment of the application also provides an electronic device that includes a bus 1110, a processor 1120, a transceiver 1130, a bus interface 1140, a memory 1150, and a user interface 1160.
In an embodiment of the present application, the electronic device further includes: computer programs stored on the memory 1150 and executable on the processor 1120, which when executed by the processor 1120, implement the various processes of the above-described embodiments of a real-time rendering doclet efficient transmission method.
A transceiver 1130 for receiving and transmitting data under the control of the processor 1120.
In an embodiment of the application, represented by bus 1110, bus 1110 may include any number of interconnected buses and bridges, with bus 1110 connecting various circuits, including one or more processors, represented by processor 1120, and memory, represented by memory 1150.
Bus 1110 represents one or more of any of several types of bus structures, including a memory bus and a memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures. By way of example, and not limitation, such an architecture includes: industry standard architecture buses, micro-channel architecture buses, expansion buses, video electronics standards association, and peripheral component interconnect buses.
Processor 1120 may be an integrated circuit chip with signal processing capabilities. In implementation, the steps of the above method embodiments may be implemented by instructions in the form of integrated logic circuits in hardware or software in a processor. The processor includes: general purpose processors, central processing units, network processors, digital signal processors, application specific integrated circuits, field programmable gate arrays, complex programmable logic devices, programmable logic arrays, micro control units or other programmable logic devices, discrete gates, transistor logic devices, discrete hardware components. The methods, steps and logic blocks disclosed in the embodiments of the present application may be implemented or performed. For example, the processor may be a single-core processor or a multi-core processor, and the processor may be integrated on a single chip or located on multiple different chips.
The processor 1120 may be a microprocessor or any conventional processor. The steps of the method disclosed in connection with the embodiments of the present application may be performed directly by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. The software modules may be located in random access memory, flash memory, read only memory, programmable read only memory, erasable programmable read only memory, registers, and the like, as known in the art. The readable storage medium is located in a memory, and the processor reads the information in the memory and, in combination with its hardware, performs the steps of the above method.
Bus 1110 may also connect together various other circuits such as peripheral devices, voltage regulators, or power management circuits, bus interface 1140 providing an interface between bus 1110 and transceiver 1130, all of which are well known in the art. Accordingly, the embodiments of the present application will not be further described.
The transceiver 1130 may be one element or a plurality of elements, such as a plurality of receivers and transmitters, providing a means for communicating with various other apparatus over a transmission medium. For example: the transceiver 1130 receives external data from other devices, and the transceiver 1130 is configured to transmit the data processed by the processor 1120 to the other devices. Depending on the nature of the computer system, a user interface 1160 may also be provided, for example: touch screen, physical keyboard, display, mouse, speaker, microphone, trackball, joystick, stylus.
It should be appreciated that in embodiments of the present application, the memory 1150 may further comprise memory located remotely from the processor 1120, such remotely located memory being connectable to a server through a network. One or more portions of the above-described networks may be an ad hoc network, an intranet, an extranet, a virtual private network, a local area network, a wireless local area network, a wide area network, a wireless wide area network, a metropolitan area network, an internet, a public switched telephone network, a plain old telephone service network, a cellular telephone network, a wireless fidelity network, and a combination of two or more of the foregoing. For example, the cellular telephone network and the wireless network may be a global system for mobile communications, a code division multiple access system, a worldwide interoperability for microwave access system, a general packet radio service system, a wideband code division multiple access system, a long term evolution system, an LTE frequency division duplex system, an LTE time division duplex system, a long term evolution advanced system, a universal mobile telecommunications system, an enhanced mobile broadband system, a mass machine class communications system, an ultra-reliable low-latency communications system, and the like.
It should be appreciated that the memory 1150 in embodiments of the present application may be either volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory. Wherein the nonvolatile memory includes: read-only memory, programmable read-only memory, erasable programmable read-only memory, electrically erasable programmable read-only memory, or flash memory.
The volatile memory includes: random access memory, which serves as an external cache. By way of example, and not limitation, many forms of RAM are available, such as: static random access memory, dynamic random access memory, synchronous dynamic random access memory, double data rate synchronous dynamic random access memory, enhanced synchronous dynamic random access memory, synchronous link dynamic random access memory, and direct memory bus random access memory. The memory 1150 of the electronic device described in embodiments of the present application includes, but is not limited to, the above and any other suitable types of memory.
In an embodiment of the application, memory 1150 stores the following elements of operating system 1151 and application programs 1152: an executable module, a data structure, or a subset thereof, or an extended set thereof.
Specifically, the operating system 1151 includes various system programs, such as: a framework layer, a core library layer, a driving layer and the like, which are used for realizing various basic services and processing tasks based on hardware. The applications 1152 include various applications such as: and the media player and the browser are used for realizing various application services. A program for implementing the method of the embodiment of the present application may be included in the application 1152. The application 1152 includes: applets, objects, components, logic, data structures, and other computer system executable instructions that perform particular tasks or implement particular abstract data types.
In addition, the embodiment of the present application further provides a computer readable storage medium, on which a computer program is stored, where the computer program when executed by a processor implements each process of the foregoing embodiment of the method for storing cost performance data in cloud rendering, and the same technical effect can be achieved, so that repetition is avoided, and no redundant description is given here.
The foregoing is merely a specific implementation of the embodiment of the present application, but the protection scope of the embodiment of the present application is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the embodiment of the present application, and the changes or substitutions are covered by the protection scope of the embodiment of the present application. Therefore, the protection scope of the embodiments of the present application shall be subject to the protection scope of the claims.
Claims (8)
1. A cost-effective data storage method in cloud rendering, wherein the method comprises:
acquiring cloud rendering data to be stored;
setting the use frequency omega of planning resources based on application scenes of data 1 ；
Extracting characteristic elements and basic overturning heat alpha of data;
setting theoretical resource access frequency omega according to characteristic elements 2 ；
Obtaining the actual resource access frequency omega;
the data access heat F is calculated and,where K is the frequency, ω ', of the data' 1 ＝ω 1 modΩ，ω′ 2 ＝ω 2 modΩ；
Comparing F with alpha, if F is larger than alpha, switching the storage space from a large space to a small space, otherwise, switching the storage space from the small space to the large space;
the obtaining cloud rendering data to be stored includes:
classifying cloud rendering data to be storedFive types of data: model data, trajectory data, evolution data, composition data, and interface data; where K is the frequency of classification data, k=k 1 k 2 k 3 k 4 k 5 ；
The feature elements of the extracted data comprise associated feature elements extracted and stored according to preset relevance;
the calculating the data access heat F includes:
judging whether the three-frequency resonance standard is reached or not according to the calculated data access heat F value; if the three-frequency resonance standard is not met, performing feature element iterative optimization, and resetting the planning resource use frequency omega 1 And theoretical resource access frequency omega 2 Until the three-frequency resonance standard is reached;
the judgment standard of the three-frequency resonance is as follows: K/F is more than or equal to 0.9 and less than or equal to 1;
the basic overturning heat alpha is gradually formed, a fixed basic value is firstly established, then overturning test analysis is carried out, and the basic overturning heat alpha is continuously optimized, so that the stable overturning heat with lower jumping cost is obtained.
2. A method of cost-effective data storage in cloud rendering as claimed in claim 1, wherein said storage space comprises:
ice space, cold space, and hot space; the spatial size relationship of the three spaces is as follows: ice space > cold space > hot space.
3. The method for storing cost performance data in cloud rendering according to claim 2, wherein comparing F with α, if F > α, the storage space is switched from large space to small space, otherwise, after the storage space is switched from small space to large space, further comprising:
and analyzing the user by adopting a mode priority principle, and optimizing a storage scheme according to the requirements of the user on the storage price and performance.
4. A method of cost effective data storage in cloud rendering as claimed in claim 2, wherein,
when data is stored in the ice space, the data is classified and compressed.
5. The method for storing cost performance data in cloud rendering according to claim 2, wherein the cold space is divided into a cold storage space and a cold storage space, and the spatial size relationship between the cold storage space and the cold storage space is: the refrigerating space is larger than the cold storage space.
6. A cost effective data storage system in cloud rendering, wherein the system comprises:
the cloud rendering device comprises a first obtaining unit, a second obtaining unit and a storage unit, wherein the first obtaining unit is used for obtaining cloud rendering data to be stored;
a first setting unit for setting a planned resource usage frequency ω based on an application scenario of the data 1 ；
A first extraction unit for extracting feature elements and a basic flip heat α of data;
a second setting unit for setting a theoretical resource access frequency omega based on the feature elements 2 ；
A second obtaining unit, configured to obtain an actual resource access frequency Ω;
a first calculation unit for calculating a data access heat F,where K is the frequency, ω ', of the data' 1 ＝ω 1 modΩ，ω' 2 ＝ω 2 modΩ；
A first comparing unit for comparing F with α;
the first execution unit is used for switching the storage space from a large space to a small space if F & gtalpha after the first comparison unit judges that F & gtalpha, otherwise, switching the storage space from the small space to the large space;
the obtaining cloud rendering data to be stored includes:
classifying cloud rendering data to be stored into five types of data: model data, trajectory data, evolution data, composition data, and interface data; where K is the frequency of classification data, k=k 1 k 2 k 3 k 4 k 5 ；
The feature elements of the extracted data comprise associated feature elements extracted and stored according to preset relevance;
the calculating the data access heat F includes:
judging whether the three-frequency resonance standard is reached or not according to the calculated data access heat F value; if the three-frequency resonance standard is not met, performing feature element iterative optimization, and resetting the planning resource use frequency omega 1 And theoretical resource access frequency omega 2 Until the three-frequency resonance standard is reached;
the judgment standard of the three-frequency resonance is as follows: K/F is more than or equal to 0.9 and less than or equal to 1;
the basic overturning heat alpha is gradually formed, a fixed basic value is firstly established, then overturning test analysis is carried out, and the basic overturning heat alpha is continuously optimized, so that the stable overturning heat with lower jumping cost is obtained.
7. A cost effective data storage system in cloud rendering comprising a bus, a transceiver, a memory, a processor and a computer program stored on the memory and executable on the processor, the transceiver, the memory and the processor being connected by the bus, characterized in that the computer program when executed by the processor implements the steps in the cost effective data storage method in cloud rendering as claimed in any one of claims 1-5.
8. A computer readable storage medium having stored thereon a computer program, which when executed by a processor performs the steps in the cost effective data storage method in cloud rendering according to any of claims 1-5.
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