CN111951363A - Cloud computing chain-based rendering method and system and storage medium - Google Patents
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Abstract
The invention provides a rendering method, a system and a storage medium based on a cloud computing chain, wherein the method comprises the following steps: sending a rendering task request to a server node; acquiring rendering task blocks executed in parallel; rendering according to the rendering task block to obtain a rendering result; extracting a plurality of rendering results for integration, and returning the integrated results to the server node; the method comprises the steps that a task request is actively provided according to rendering nodes, a large number of task blocks generated after task processing and splitting are reasonably distributed to each available rendering node in a network, powerful decentralized computing resources are provided for high-strength computing type tasks by using the idle states of the rendering nodes, and the data processing efficiency is higher; meanwhile, through an active request and distribution matching mechanism, the distribution of the computing task is more reasonable, and further the rendering node autonomy without difference is realized, so that the method can be widely applied to the technical field of distributed computing networks.
Description
Technical Field
The invention relates to the technical field of computer image processing, in particular to a rendering method, a rendering system and a storage medium based on a cloud computing chain.
Background
Rendering in computer graphics refers to the process of generating images from models with software. A rendering model is a description of a three-dimensional object in a well-defined language or data structure that includes geometric, viewpoint, texture, and lighting information.
In the prior art, a single computer is generally adopted to complete rendering, and the rendering needs to be performed for a long time through one end and occupies most of computing resources, so that other production activities cannot be performed synchronously; secondly, when facing high-intensity computing type tasks, a centralized computing mode is adopted, and a central computer needs to execute all operations, so that when a plurality of terminals exist, the response speed is reduced; if the end users have different task requirements, the programs and resources of each user need to be configured separately, which is difficult to be realized on a centralized system and has low efficiency. In addition, in some rendering farms in the prior art, a manner of balanced distribution of rendering tasks is simply adopted, and without considering the performance state of each terminal and whether other tasks occupy computing resources, pressure on computing resource calling is also caused on a single terminal node in the rendering farm.
Disclosure of Invention
In view of the above, to at least partially solve one of the above technical problems, embodiments of the present invention provide an efficient cloud-computing-chain-based rendering method, and a system, a storage medium of a rendering node, and a storage medium of a server node, which can implement cloud-computing-chain-based rendering.
In a first aspect, the invention provides a cloud computing chain-based rendering method, which includes the following steps:
sending a rendering task request to a server node;
acquiring rendering task blocks executed in parallel, wherein the rendering task blocks are obtained by distributing acquired tasks to be rendered according to rendering task requests by server nodes; the files of the task to be rendered comprise video files and image files;
rendering according to the rendering task block to obtain a rendering result;
and extracting a plurality of rendering results for integration, and returning the integrated rendering results to the server node.
Furthermore, in some embodiments of the invention, the method further comprises the steps of: acquiring computing power resources consumed by rendering according to the rendering task block, and generating an actual workload certificate according to the consumed computing power resources; the actual workload proves to be the amount of work that the computing resources are able to do without performing the rendering task.
In a second aspect, the present invention provides another cloud computing chain-based rendering method, including the following steps:
the method comprises the steps of obtaining a rendering task request, a task to be rendered and parallelism of a rendering node, and dividing the task to be rendered according to the parallelism to obtain a plurality of rendering task blocks which are executed in parallel; the files of the rendering task comprise a video file and an image file;
distributing the rendering task block to a plurality of rendering nodes according to the rendering task request and the performance parameters of the rendering nodes; the state parameters of the rendering nodes comprise the disposable rendering calculation power, the storage space and the broadband environment of the rendering nodes;
acquiring a first rendering result, verifying the first rendering result to obtain a second rendering result, and outputting the second rendering result; the first rendering result is an integrated rendering result which is obtained by extracting a plurality of rendering results from the rendering nodes by the rendering nodes for integration and returning.
In some embodiments of the invention, the method further comprises the steps of:
performing performance test on the rendering node, and recording a performance test result;
acquiring the relative position of the rendering node performance in the rendering node performance of the whole network;
and generating rendering time for executing the task block according to the performance test result and the relative position, and updating the performance parameters of the rendering nodes.
In some embodiments of the invention, the method further comprises the steps of: and when the first rendering result is not obtained, determining that rendering fails and allocating the rendering task block to a new rendering node based on a dynamic reallocation mechanism.
In some embodiments of the present invention, the step of dividing the task to be rendered according to the parallelism to obtain a plurality of rendering task blocks to be executed in parallel is obtained, and the method specifically includes the following steps:
acquiring the sum of pixels in a file of a task to be rendered as a first pixel number; determining a second pixel number according to the first pixel number and the parallelism, wherein the second pixel number is the sum of pixels in the divided rendering task block;
and when the number of the pixels in the rendering task block obtained after the segmentation is less than the second number of the pixels, performing pixel filling on the rendering task block until the number of the pixels in the rendering task block is equal to the second number of the pixels.
In some embodiments, the step of allocating a rendering task block to a plurality of rendering nodes according to a rendering task request and performance parameters of the rendering nodes; the method specifically comprises the following steps:
determining rendering computational resources occupied by the rendering task block;
performing rendering node performance matching according to rendering computational resources occupied by the rendering task blocks, and determining sending frequency;
and distributing the task blocks to a plurality of rendering nodes according to the performance matching result and the sending frequency of the rendering nodes.
In a third aspect, a technical solution of the present invention further provides a rendering system based on a cloud computing chain, including a rendering node and a server node:
the rendering node is used for sending a rendering task request to the server node; acquiring rendering task blocks executed in parallel, wherein the rendering task blocks are obtained by distributing acquired tasks to be rendered according to rendering task requests by server nodes; the files of the task to be rendered comprise video files and image files; rendering according to the rendering task block to obtain a rendering result; and extracting a plurality of rendering results for integration, and returning the integrated rendering results to the server node.
The server node acquires a rendering task request of the rendering node, a task to be rendered and the parallelism of the task to be rendered, and divides the task to be rendered according to the parallelism to obtain a plurality of rendering task blocks which are executed in parallel; the files of the rendering task comprise a video file and an image file; distributing the rendering task block to a plurality of rendering nodes according to the rendering task request and the performance parameters of the rendering nodes; the state parameters of the rendering nodes comprise the disposable rendering calculation power, the storage space and the broadband environment of the rendering nodes; acquiring a first rendering result, verifying the first rendering result to obtain a second rendering result, and outputting the second rendering result; the first rendering result is an integrated rendering result returned by the rendering node.
In a fourth aspect, a technical solution of the present invention further provides another cloud computing chain-based rendering system, which includes:
at least one processor;
at least one memory for storing at least one program;
when the at least one program is executed by the at least one processor, the at least one processor is caused to implement one of the cloud computing chain-based rendering methods of the first aspect or the second aspect.
In a fifth aspect, the present invention also provides a storage medium, in which a processor-executable program is stored, and the processor-executable program is used to implement the method in the first aspect or the second aspect when executed by a processor.
Advantages and benefits of the present invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention:
according to the invention, a rendering task request is actively provided according to the rendering nodes, a large number of rendering task blocks generated after the task to be rendered is split are distributed to each rendering node in the network, and the idle computing resources of the rendering nodes in the cloud computing chain are utilized to provide strong decentralized rendering capability for the high-strength rendering task, so that the processing efficiency of the rendering task is higher; meanwhile, through an active request and distribution matching mechanism of the rendering nodes, the distribution of the calculation tasks is more reasonable, and the autonomy of the rendering nodes without difference is further realized.
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In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a schematic diagram of a cloud computing chain system according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating steps performed by a rendering node based on a cloud computing chain according to an embodiment of the present invention;
fig. 3 is a flowchart illustrating steps executed by a server node based on a cloud computing chain according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention. The step numbers in the following embodiments are provided only for convenience of illustration, the order between the steps is not limited at all, and the execution order of each step in the embodiments can be adapted according to the understanding of those skilled in the art.
A cloud computing chain is a distributed computing network for handling high-intensity computing-type tasks that cannot be completed by a single computer. In a cloud computing chain, idle computing power of various intelligent devices can be accessed to a decentralized computing network, and application in computing tasks is achieved by combining a large number of algorithms and engineering optimization. On the first hand, as shown in fig. 1, a Cloud Computing Chain (Cloud Computing Chain) in the present embodiment belongs to a distributed Computing network of a "hierarchical decentralized structure", and each different module is defined as a different level; in the same level, complete decentralization is realized; when the mixing is carried out in multiple stages, the structure is multicenter. The cloud Computing chain adopts a Client-Server Architecture (Client-Server Architecture), the Computing Resource Provider (Computing Resource Provider) has a Client role including a distributed rendering Node and a distributed storage Node, and the Server Node (Service Node) has a Server role. When the rendering node is in an idle state, the idle state of the rendering node is actively uploaded to the server node at the same level, and the server node matches and distributes corresponding task blocks according to the performance characteristics of computing resources. In addition, the cloud computing chain can also directly pre-store the divided rendering task blocks into distributed storage nodes of the cloud computing chain, and the distributed rendering nodes download data of the rendering task blocks from the distributed storage nodes after receiving a scheduling instruction of the server. Under a distributed network topology, computationally-efficient resource providers and server nodes can be incorporated into or removed from the network at any time.
In a second aspect, referring to fig. 2, an embodiment provides a cloud computing chain-based rendering method, which includes steps S101 to S104, executed by a rendering node in a cloud computing chain:
s101, sending a rendering task request to a server node; in the cloud computing chain of the embodiment, distributed rendering nodes in each level send the idle condition of the rendering node and the performance condition of the rendering node to a server node according to the idle condition of computing resources of the node; or the rendering node is executing the task with smaller calculation amount, and sufficient distributable calculation resources are provided to the cloud computing chain to serve as the basis for the subsequent server node to distribute and match the rendering task block.
S102, obtaining rendering task blocks executed in parallel, wherein the rendering task blocks are obtained by distributing rendering task files with huge calculation amount obtained by server nodes according to task requests; the files of the task to be rendered include video files and image files.
S103, rendering is carried out according to the rendering task block to obtain a rendering result; for example, a rendering node completes rendering of a frame of animation video, and first passes through a three-dimensional geometric model, three-dimensional animation definition information and corresponding material information. And generating an image of the video frame through geometric transformation, projection transformation, perspective transformation and window clipping and through the acquired material and light and shadow information. After the image rendering is finished, the image information of the video frame is output to the local image file or video file of the rendering node, or the frame image is previewed in the frame buffer of the rendering node.
S104, extracting a plurality of rendering results for integration, and returning the integrated results to the server node; and splicing and combining the rendered images and transmitting the images back to the server node.
In a third aspect, referring to fig. 3, an embodiment of the present invention provides another rendering method based on a cloud computing chain, where a task to be rendered is submitted to the cloud computing chain, the task is divided into a large number of rendering task blocks that are executed in parallel, the task blocks are distributed to distributed rendering nodes, and after the processes of node rendering, integration and verification of rendering results of each node, and the like, a rendered image or video is finally obtained. The specific steps include S201-S203, which are completed and executed by a server node in a cloud computing chain:
s201, a rendering task request of a rendering node and the parallelism of a task to be rendered of the task to be rendered are obtained, and the task to be rendered is divided according to the parallelism to obtain a plurality of rendering task blocks which are executed in parallel; specifically, the task to be rendered may be a single picture or a video file, for example, the video task to be rendered is submitted to a cloud computing chain, the cloud computing chain first performs frame-by-frame division of the video file, analyzes the type of the task and the specific content of the task, and divides the frame-by-frame image rendering task into a plurality of pixel blocks, that is, rendering task blocks that are executed in parallel, according to the type and content of the task, matching a corresponding division algorithm and a preset parallelism. In addition, in the cloud computing chain of the embodiment, basic segmentation algorithm support is provided, and segmentation algorithms suitable for different tasks can be developed or called by self based on the CCC API, for example: a segmentation algorithm supporting CGI rendering type tasks.
In some embodiments, step S201 may further include step S2011 and step S2012:
s2011, acquiring the sum of pixels in a file of a task to be rendered as a first pixel number; and determining a second pixel number according to the first pixel number and the parallelism of the task to be rendered, wherein the second pixel number is the sum of pixels in the segmented rendering task block. Specifically, before the division processing of the rendering task is performed, the number of pixels included in each task block may be set, and the rendering task is divided according to the set number of pixels and the parallelism, where the parallelism is the number of the selected rendering nodes.
S2012, when the number of the pixels in the rendering task block obtained after the division is less than the second number of the pixels, performing pixel filling on the rendering task block until the number of the pixels in the rendering task block is equal to the second number of the pixels; specifically, in the segmentation process, when the number of the segmented pixels in the task block is less than the preset number of pixels, the missing pixels are filled by the gray pixels.
S202, distributing the rendering task block to a plurality of rendering nodes according to the rendering task request and the performance parameters of the rendering nodes. Specifically, a server node distributes a large number of rendering task blocks generated after processing and splitting of a task to be rendered to each available rendering node in a network, so that optimal distribution is guaranteed; in order to ensure optimal allocation, in the process of task block allocation by the server node, the task request from the rendering node received by the server node and the performance parameters of the corresponding rendering node are used as the main standard for distribution matching. Wherein the performance parameters of the rendering nodes comprise: and the rendering nodes can control the comprehensive indexes of parameters such as rendering computing power, storage space, broadband environment and the like.
More specifically, step S202 also includes steps S2021-S2023 in some embodiments:
s2021, before the task blocks are distributed, the computing power resources occupied by the rendering task blocks are pre-estimated by the server node.
S2022, performing rendering node performance matching according to rendering computational resources occupied by the rendering task blocks, and determining the sending frequency of the task blocks.
S2023, distributing the task blocks to a plurality of rendering nodes according to the performance matching result of the rendering nodes and the sending frequency.
Specifically, a server node in the cloud computing chain predicts computing resources required to be occupied by the divided task blocks, then performs matching according to the current computing power occupation condition and state performance parameters of each rendering node in the cloud computing chain, and adjusts the transmission frequency of the task blocks according to the computing power occupation condition of the nodes. In addition, the embodiment can also recognize and record hardware information of all rendering nodes in the cloud computing chain, and in order to avoid computing result difference caused by hardware difference, a plurality of task blocks generated after the same task is split can be set to be sent to the same type of hardware equipment in the network for computing. As an optional implementation manner, the server node may consider multi-dimensional parameters such as task types, task block calculation amount, node calculation force, node network conditions, node historical stability, node activity and the like according to an allocation model of an artificial intelligence algorithm; and completing distribution matching of task blocks through deep learning.
S203, acquiring a first rendering result, splicing the first rendering result to obtain a second rendering result, and outputting the second rendering result; the first rendering result is a result of the rendering node after completion of the verification.
Specifically, after images with continuous sequence rendering task blocks are randomly extracted from rendering nodes at the same level in the cloud computing chain and spliced and integrated, whether the rendering process meets the verification rule is verified. The rules for validation in the embodiment include: firstly, in a cloud computing chain, the number of rendering nodes for executing rendering tasks according to rendering task blocks is not less than a first threshold value; secondly, executing the rendering task according to the rendering task block, and generating the number of rendering image results which is not less than a second threshold value; the first threshold value is a preset rendering node number; the second threshold is a preset number of rendering image results. For example, in one embodiment, for computing result correctness, the result verification condition provided needs to satisfy two rules: the first rendering task block and the same rendering task block are sent to at least A (A is more than or equal to 3) rendering nodes in an idle state; and the second rendering task block successfully returns the calculation result not less than B. The values of a and B may be defined according to the specific application scenario, and permissions may also be opened to the computing resource consumers. For another example: in the CG rendering task, a is defined as 5, and B is defined as 51%, that is, when the result from 3 rendering nodes in the same task block is successfully returned and the comparison and verification are successful, the task block result is labeled as "successful-to-be-integrated". In addition, according to the rendering task blocks with different task contents, the check rule of the rendering result is expanded, and the check rule can also set the number of times that the task blocks in a single rendering node are executed and iterated, and the like. The cloud computing chain in this embodiment may also be developed and deployed based on an API for an algorithm used by its application.
In some embodiments, the server node records the computation Work done by the rendering node by using a Proof of actual workload (Proof-of-actual-Work) mechanism, and generates and stores a corresponding record file. Different from the current common mining application, the computing resources of the intelligent equipment when idle are used for the specific computing task which is finished in the real world and generates the actual value, the actual value of the computing task is recorded, namely the actual workload is proved, and an actual workload proving mechanism is adopted in the embodiment; for example, a certain terminal device completes a production task within a period of time and generates an actual value with a size of a, and then the cloud computing chain calculates the value generated by the terminal in unit time according to the period of time and the value of a, which is recorded as an actual work proof of the terminal; distributing the same task block to at least 3 rendering nodes, randomly distributing the same task block in the rendering nodes meeting the matching condition, and enabling different rendering nodes to enter a verification process for a task result obtained after the same rendering task is rendered. In order to prevent the false reporting of task computing time and the performance condition of a node, a cloud computing chain in the embodiment starts a reference point (Benchmark) dynamic adjustment mechanism; based on the records of the performance tests of the rendering nodes by different Benchmark, the relative position of the performance of a single device in the performance of the cloud computing chain whole-network rendering node device is mastered, the reasonability of the computing time of the task block is judged by taking the relative position as a reference, and meanwhile, the performance parameters of the rendering nodes are tested and updated at irregular time.
In some embodiments of the cloud Computing chain, Redundant Computing (Redundant Computing) and Dynamic Redistribution (Dynamic Redistribution) techniques are also employed to ensure the integrity of all task renderings; the dynamic reallocation mechanism can realize the reallocation of the task blocks of the rendering nodes to the rendering nodes with idle other computational resources. And the redundant computing mechanism ensures that one rendering task block is sent to N rendering nodes for computing, and the number of the redundant nodes can be determined in a user-defined mode for different types of tasks. For example: if a certain rendering node fails to successfully submit the rendering result of the task block within the specified time, the node is determined to fail to render; at the same time, the dynamic reallocation mechanism will be activated, and the rendering task block of the node will be allocated to the new rendering node.
In addition, the cloud computing chain is used as a novel decentralized super computing network, has extremely strong protocol attributes, can run on a alliance chain, and can rapidly integrate technologies such as transaction accounting, encryption and intelligent contracts of a block chain. Meanwhile, a standardized API (application programming interface) is opened for a developer community, and powerful decentralized computing resources are provided for various upper-layer applications.
According to the contents of fig. 2 and fig. 3, taking the task of complete animation movie rendering as an example, the animation video file and the rendering task are submitted to the cloud computing chain, and the number of rendering nodes required in the rendering work is determined, meanwhile, according to the rendering task, the video file to be rendered is decomposed frame by frame, and the frame by frame picture is further disassembled, as described in step S201, a plurality of task blocks to be rendered which can be executed in parallel are obtained, and the data is distributed to a preset number of idle distributed rendering nodes through the step S202, the rendering nodes are handed to complete the rendering work in the steps S101-S104, after the result verification of the rendering task block is completed by each rendering node, and returning the result to the server node, completing verification and frame-by-frame splicing of the file rendering result by the server node as described in the step S203, and outputting to obtain the rendered video file.
In a fourth aspect, an embodiment of the present invention further provides another cloud computing chain-based rendering system, where core elements in the system include a rendering node and a server node:
the rendering node is used for sending a task request to the server node; acquiring a task block which is executed in parallel, wherein the task block is obtained by dividing an acquired computing task by a server according to a task request; executing a rendering task according to the task block to generate a calculation result; and verifying the calculation result and returning the verified result.
The server node is used for acquiring a computing task and a task request of the rendering node, and dividing the computing task into a plurality of task blocks which are executed in parallel; distributing the task block to a plurality of rendering nodes according to the task request and the performance parameters of the rendering nodes; the state parameters of the rendering node include the computing power, storage space, and broadband environment of the rendering node. And acquiring a calculation result, integrating the calculation result, outputting the integrated calculation result, and finishing the verified calculation result by the rendering node executing the calculation task according to the task block.
In some system embodiments, the system comprises a plurality of server nodes, the system ensures that the integral execution of the task is not seriously affected by the fault of a single server node through a fault tolerance mechanism, and the normally running server node dynamically takes over the task of the fault node to avoid the occurrence of single point fault (SPoF).
The contents in the method embodiments of the second aspect and the third aspect are applicable to the embodiments of the present system, the functions implemented by the embodiments of the present system are the same as those in the above method embodiments, and the advantageous effects achieved by the embodiments of the present system are also the same as those achieved by the above method embodiments.
In a fifth aspect, an embodiment of the present invention further provides a cloud computing chain-based rendering system, which includes at least one processor; at least one memory for storing at least one program; when the at least one program is executed by the at least one processor, the at least one processor may implement a cloud-daisy-chain based rendering method as illustrated in fig. 2 or fig. 3.
The embodiment of the invention also provides a storage medium with a program stored therein, and the program is executed by a processor to perform the method shown in fig. 2 or fig. 3.
From the above specific implementation process, it can be concluded that the technical solution provided by the present invention has the following advantages or advantages compared to the prior art:
1. the method completes video or image file rendering based on the cloud computing chain, calls more thread computing resources, doubles the rendering speed, and greatly reduces the time consumed by rendering work.
2. The technical scheme provided by the invention combines the advantages of a centralized computing network and the advantages of a distributed computing network, and realizes complete decentralization in the same level; when the mixing is carried out in multiple stages, the structure is a multi-center structure; the method not only ensures high efficiency, high performance and good fault tolerance performance and expansibility, but also can avoid the safety problem and the sharing risk problem of data.
In alternative embodiments, the functions/acts noted in the block diagrams may occur out of the order noted in the operational illustrations. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality/acts involved. Furthermore, the embodiments presented and described in the flow charts of the present invention are provided by way of example in order to provide a more thorough understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed and in which sub-operations described as part of larger operations are performed independently.
Furthermore, although the present invention is described in the context of functional modules, it should be understood that, unless otherwise stated to the contrary, one or more of the functions and/or features may be integrated in a single physical device and/or software module, or one or more of the functions and/or features may be implemented in a separate physical device or software module. It will also be appreciated that a detailed discussion of the actual implementation of each module is not necessary for an understanding of the present invention. Rather, the actual implementation of the various functional modules in the apparatus disclosed herein will be understood within the ordinary skill of an engineer, given the nature, function, and internal relationship of the modules. Accordingly, those skilled in the art can, using ordinary skill, practice the invention as set forth in the claims without undue experimentation. It is also to be understood that the specific concepts disclosed are merely illustrative of and not intended to limit the scope of the invention, which is defined by the appended claims and their full scope of equivalents.
Wherein the functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the invention have been shown and described, it will be understood by those of ordinary skill in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.
While the preferred embodiments of the present invention have been illustrated and described, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. A cloud computing chain-based rendering method is characterized by comprising the following steps:
sending a rendering task request to a server node;
acquiring rendering task blocks executed in parallel, wherein the rendering task blocks are obtained by distributing acquired tasks to be rendered according to rendering task requests by the server nodes; the files of the task to be rendered comprise video files and image files;
rendering according to the rendering task block to obtain a rendering result;
and extracting a plurality of rendering results for integration, and returning the integrated rendering results to the server node.
2. The cloud computing chain-based rendering method according to claim 1, further comprising the steps of:
acquiring computing power resources consumed by rendering according to the rendering task block, and generating an actual workload certificate according to the consumed computing power resources; the actual workload proves to be the workload that the computing resources are able to do without performing the rendering task.
3. A cloud computing chain-based rendering method is characterized by comprising the following steps:
the method comprises the steps of obtaining a rendering task request, a task to be rendered and parallelism of a rendering node, and dividing the task to be rendered according to the parallelism to obtain a plurality of rendering task blocks which are executed in parallel; the files of the rendering task comprise video files and image files;
distributing the rendering task block to a plurality of rendering nodes according to the rendering task request and the performance parameters of the rendering nodes; the state parameters of the rendering nodes comprise the disposable rendering calculation power, the storage space and the broadband environment of the rendering nodes;
obtaining a first rendering result, verifying the first rendering result to obtain a second rendering result, and outputting the second rendering result; and the first rendering result is an integrated rendering result which is obtained by extracting a plurality of rendering results from the rendering nodes for integration and returning.
4. The cloud computing chain-based rendering method according to claim 3, further comprising the steps of:
performing performance test on the rendering node, and recording a performance test result;
acquiring the relative position of the performance of the rendering node in the performance of the rendering node of the whole network;
and generating rendering time for executing the task block according to the performance test result and the relative position, and updating the performance parameters of the rendering nodes.
5. The cloud computing chain-based rendering method according to claim 3, further comprising the steps of:
and when the first rendering result is not obtained, determining that rendering fails and distributing the rendering task block to a new rendering node based on a dynamic redistribution mechanism.
6. The cloud computing chain-based rendering method according to any one of claims 3 to 5, wherein the step of segmenting the task to be rendered into a plurality of rendering task blocks executed in parallel according to the parallelism specifically includes:
acquiring the sum of pixels in the file of the task to be rendered as a first pixel number; determining a second pixel number according to the first pixel number and the parallelism, wherein the second pixel number is the sum of pixels in the divided rendering task block;
and when the number of the pixels in the rendering task block obtained after the division is smaller than the second number of the pixels, performing pixel filling on the rendering task block until the number of the pixels in the rendering task block is equal to the second number of the pixels.
7. The cloud computing chain-based rendering method according to any one of claims 3 to 5, wherein the step of allocating the rendering task block to a plurality of rendering nodes according to the rendering task request and performance parameters of the rendering nodes; the method specifically comprises the following steps:
determining rendering computational resources occupied by the rendering task block;
performing rendering node performance matching according to rendering computational resources occupied by the rendering task blocks, and determining sending frequency;
and distributing the task block to a plurality of rendering nodes according to the performance matching result of the rendering nodes and the sending frequency.
8. A cloud computing chain-based rendering system is characterized by comprising a rendering node and a server node:
the rendering node is used for sending a rendering task request to the server node; the method comprises the steps of obtaining rendering task blocks executed in parallel, wherein the rendering task blocks are obtained by distributing obtained tasks to be rendered according to rendering task requests by server nodes; the files of the task to be rendered comprise video files and image files; rendering according to the rendering task block to obtain a rendering result; extracting a plurality of rendering results for integration, and returning the integrated rendering results to the server node; the server node acquires a rendering task request, a task to be rendered and a parallelism of the rendering node, and divides the task to be rendered according to the parallelism to obtain a plurality of rendering task blocks which are executed in parallel; the files of the rendering task comprise video files and image files; distributing the rendering task block to a plurality of rendering nodes according to the rendering task request and the performance parameters of the rendering nodes; the state parameters of the rendering nodes comprise the disposable rendering calculation power, the storage space and the broadband environment of the rendering nodes; acquiring a first rendering result, verifying the first rendering result to obtain a second rendering result, and outputting the second rendering result; and the first rendering result is an integrated rendering result which is obtained by extracting a plurality of rendering results from the rendering nodes for integration and returning.
9. A cloud computing chain-based rendering system, comprising:
at least one processor;
at least one memory for storing at least one program;
when executed by the at least one processor, cause the at least one processor to implement a cloud computing chain-based rendering method as claimed in any one of claims 1 to 7.
10. A storage medium having stored therein a program executable by a processor, characterized in that: the processor-executable program when executed by a processor is for implementing a cloud computing chain based rendering method as claimed in any one of claims 1 to 7.
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