CN109671140B - Cloud rendering service processing method adopting micro-service - Google Patents
Cloud rendering service processing method adopting micro-service Download PDFInfo
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- CN109671140B CN109671140B CN201811600459.XA CN201811600459A CN109671140B CN 109671140 B CN109671140 B CN 109671140B CN 201811600459 A CN201811600459 A CN 201811600459A CN 109671140 B CN109671140 B CN 109671140B
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- 238000003672 processing method Methods 0.000 title claims abstract description 9
- 238000000034 method Methods 0.000 claims abstract description 11
- 238000013468 resource allocation Methods 0.000 claims abstract description 10
- 238000012795 verification Methods 0.000 claims abstract description 8
- 230000007246 mechanism Effects 0.000 claims abstract description 6
- 230000006798 recombination Effects 0.000 claims abstract description 5
- 238000005215 recombination Methods 0.000 claims abstract description 5
- 230000000694 effects Effects 0.000 claims description 10
- 238000012384 transportation and delivery Methods 0.000 claims description 8
- 238000012544 monitoring process Methods 0.000 claims description 7
- 230000008569 process Effects 0.000 claims description 7
- 238000012545 processing Methods 0.000 claims description 4
- 238000000354 decomposition reaction Methods 0.000 claims description 3
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- 238000004364 calculation method Methods 0.000 claims description 2
- 238000010801 machine learning Methods 0.000 claims description 2
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Classifications
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T15/00—3D [Three Dimensional] image rendering
- G06T15/06—Ray-tracing
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T3/00—Geometric image transformation in the plane of the image
- G06T3/40—Scaling the whole image or part thereof
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/10—Protocols in which an application is distributed across nodes in the network
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Abstract
The invention provides a cloud rendering service processing method adopting micro services. The method uses a micro-service mode to decompose the cloud rendering service to form a cloud rendering service consisting of eight micro-services of rendering image quality determination, rendering speed determination, parallel scheme determination, rendering resource allocation, cloud rendering, rendering verification and rendering scheme recombination; each micro service is subjected to standardized setting, and the service content, the service format, the service mode and the service sequence of the micro service are specified; the execution of each rendering micro-service at the cloud server end is determined by the combination mode, the connection mode and the torsion mechanism among the micro-services determined by the rendering tasks, so that the automatic operation of the rendering services can be realized.
Description
Technical Field
The invention belongs to the field of computer vision, and particularly relates to a cloud rendering service processing method adopting micro services.
Background
With the continuous increase of the complexity of the cloud rendering service, the continuous increase of users, the expandability of the cloud rendering service under a single organization is reduced, the parallelization capability is limited, and the automatic deployment and continuous delivery capability is poor.
The present invention aims to use a micro-service architecture to break down cloud rendering services in a service oriented concept. The decomposed cloud rendering service is composed of eight micro services, the micro services are loosely coupled, the expandability is enhanced, and the appointed service can be expanded and contracted. The single service function is cohesive, the complexity is reduced, and the split and management are convenient; the micro services can be automatically connected and freely combined, and the parallelization capability is enhanced; the single micro-service can be independently deployed, independently developed, and the automatic deployment and continuous delivery capability are enhanced.
Disclosure of Invention
The invention adopts a service-oriented design based on the idea of micro service, and forms a new cloud rendering service processing method in a micro service mode. The method can intelligently generate the customized cloud rendering scheme and complete the rendering effect satisfied by the user.
The method mainly comprises the following steps: the cloud rendering service is decomposed in a service-oriented manner by using a micro-service framework, so that a cloud rendering service consisting of eight micro-services of rendering image quality determination, rendering speed determination, parallel scheme determination, rendering resource allocation, cloud rendering, rendering verification and rendering scheme recombination is formed.
The rendering image quality determination refers to that when a remote user initiates a cloud rendering task, corresponding rendering image quality determination is performed according to the target environment of the rendering task, and a thumbnail test selection method is adopted for the user to select the image quality of a rendering scene. In order to ensure that the rendering service can deliver different rendering image quality for the same rendering requirement, the micro service firstly needs to complete automatic confirmation of the rendering image quality with a user, and secondly needs to complete dimension reduction processing on the original design according to different rendering image quality requirements. According to different display resolutions, at present, four different choices of 25%, 50%, 100% and 200% are provided, and the choice is determined by a user through a way of rendering a small sample (the choice can be directly determined by the user knowing the function); when the selection of the rendering image quality is determined, corresponding dimension reduction processing is carried out on the surface element, the material and the ray tracing force on the basis of the comprehensive analysis of the original design according to the corresponding selection. Thus, if the user currently uses a different mobile terminal to initiate a rendering task, the system can automatically reduce the rendering image quality.
The rendering speed determination refers to whether the current network speed, the concurrency, the storage bandwidth and the idle condition of the cloud rendering server meet the fluency experience requirement of the current rendering task or not in rendering, particularly real-time rendering. If the user has a certain requirement on time delay, the micro-service needs to complete the task of real-time monitoring of the delivery environment, real-time monitoring of delivery delay is performed by adding a time tag in the delivery rendering data stream, and according to the result, or more server clusters are applied to perform cloud rendering to reduce rendering time, or more display caches are applied to reduce rendering blockage.
The parallel scheme determination is to perform corresponding parallelization processing on the cloud rendering task for completing the requirements of image quality and speed on the basis of two micro services, namely rendering image quality determination and rendering speed determination. The micro-service has three tasks, namely, available rendering calculation resources are monitored, an optimized rendering parallelism parameter is given on the basis of the first rendering cost, the rendering tasks are divided on the basis of the second rendering cost, and the principle of dividing is that equivalent surface elements are basically equally divided, namely: and for the dimension-reduced rear elements, under the background that the ray tracing force is similar and the rendering material basis is the same, the average division is realized, and the consistency of the rendering process is striven for. Each decomposed graph automatically generates a thumbnail with a corresponding mark. The rendering resources required for the decomposed graphic areas are different according to the difference of the rendering effects. And determining the rendering resources required by each rendering area and the storage position of the rendering resources to form a parallel scheme.
The rendering resource allocation means that after the parallel scheme is formed, the micro-service firstly generates a refined execution scheme, secondly prepares and matches tasks and resources for the rendering server on the basis of the micro-service, and completes the setting of corresponding rendering parameters.
The rendering resource allocation refers to the allocation of rendering resources according to the set rendering parameters, and cloud rendering is started after the rendering resource allocation is completed.
Cloud rendering is to monitor the rendering process after the rendering resource is loaded, and to realize the elastic circulation of tasks under the condition of exception, and to form a rendering resource network after analyzing the rendering file. The cloud rendering server loads rendering resources to the rendering model according to the rendering resource network and then renders the rendering resources.
The rendering verification means that after the rendering is completed, the marked area after the rendering is compared with the thumbnail corresponding to the mark to verify whether the rendering effect expected by the user is achieved or not.
The reconstruction of the rendering scheme refers to that after the cloud rendering task verification service is completed, if the rendering effect does not meet the requirement of a user, micro-service reconstruction is performed, a new rendering resource network is established, a new parallel scheme is formed, and rendering is performed again.
For the micro-service facing rendering, we have standardized definition, which includes four basic specifications of service content, service quality, service mode and service sequence, and two expansion specifications of custom and standby. Each micro service has a respective calling mode, so that the micro services can be flexibly called on the premise of being mutually independent, and in the micro services, the standardized definition of the micro services plays a key role, so that the system can accurately distinguish, use and manage different micro services. The service content prescribes the name and the service boundary of the micro-service, the service quality prescribes the priority level and the service assessment requirement of the micro-service, the service mode prescribes the basic element operation and the concurrency index of the micro-service, and the service sequence prescribes the service serial number and the dependency relationship of the micro-service. In order to make the service open, two expansion regulations of custom and reserve regulations are specially set, the custom regulation is a macro regulation, the micro service flow can be simplified by properly combining partial element operations in the existing micro service, and the reserve regulations are reserved regulations and reserved for system upgrading. In micro-service invocation, a micro-service registration and discovery mechanism is used to achieve automatic engagement between micro-services.
The rendering server registers the micro-services according to the current cloud rendering task, and determines the connection mode between the micro-services. The cloud rendering server will manage these micro services and automatically circulate the micro services through a micro service discovery mechanism.
After the rendering is completed, the rendering result is checked as necessary. After the current cloud rendering is completed, comparing a plurality of two-dimensional image thumbnails reflecting the three-dimensional scene reserved in the cloud rendering resource request service with corresponding two-dimensional image marking areas after the current cloud rendering task is completed. If the graphic rendering effect expected by the user is not achieved, the cloud rendering server reorganizes the next cloud rendering scheme according to the current cloud rendering result, and then produces a new cloud rendering scheme. And this new cloud rendering scheme is automatically generated by intelligent reorganization strategy based on machine learning. The cloud rendering server generates different rendering schemes according to different rendering tasks until the graphic rendering effect expected by the user is achieved.
Drawings
Fig. 1 is a diagram of a rendering service microservice composition of the present invention.
Fig. 2 is a schematic diagram of the microservice communication of the present invention.
Fig. 3 is a flowchart of an automatic process of the cloud rendering service according to the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
In fig. 1, a rendering service microservice composition diagram of the present invention is represented. The cloud rendering service decomposed in the micro-service mode comprises eight cloud rendering micro-service components including rendering image quality determination, rendering speed determination, parallel scheme determination, rendering resource allocation, cloud rendering, rendering verification and rendering scheme recombination as shown in the figure.
In fig. 2, a micro-service communication schematic of the present invention is represented. In this figure, instance a is a rendering quality determination, instance B is a rendering speed determination, instance C is a parallel scheme determination, and instance D is a rendering resource allocation. After the cloud rendering service decomposition is completed and the service content is defined for the micro-services, the service format and the calling mode are defined for each micro-service. When each micro service is started, registering information such as own network address and the like into a service discovery component, and storing the information by the service discovery component; the cloud rendering server may query network addresses of the respective micro services from the service discovery component and call the micro services using the addresses; each micro-service communicates with a service discovery component using heartbeat, and if the service discovery component fails to communicate with a micro-service instance for a long period of time, the instance is logged off.
In fig. 3, the automatic cloud rendering service processing method of the present invention is expressed, and the specific steps are as follows:
(1) The cloud rendering service is decomposed into eight micro services and standardized setting is carried out;
(2) Determining the micro-service composition and the connection mode according to the rendering file, establishing a rendering resource network, and generating a parallel scheme;
(3) Rendering according to a parallel scheme;
(4) After rendering is completed, checking;
(5) If the expected rendering effect is met, completing rendering;
(6) If not, performing micro-service recombination, and making a new rendering parallel scheme to re-render until the micro-service is qualified.
Claims (3)
1. The cloud rendering service processing method adopting the micro service adopts the micro service method and uses a service-oriented mode to decompose the cloud rendering service, and is characterized in that: after decomposition, forming a cloud rendering service consisting of eight micro services of rendering image quality determination, rendering speed determination, parallel scheme determination, rendering resource allocation, cloud rendering, rendering verification and rendering scheme recombination;
the rendering image quality determination means that when a remote user initiates a cloud rendering task, corresponding rendering image quality determination is carried out according to the target environment of the rendering task, and a thumbnail test selection method is adopted for the user to select the image quality of a rendering scene;
the rendering speed determination refers to whether the current network speed, the concurrency, the storage bandwidth and the idle condition of the cloud rendering server meet the fluency experience requirement of the current rendering task or not in the rendering process, particularly in real time; if the user has a requirement on time delay, the micro-service needs to complete the task of real-time monitoring of the delivery environment, real-time monitoring of delivery delay is performed by adding a time tag in the delivery rendering data stream, and rendering time is reduced or rendering blockage is reduced by applying for more display caches according to the real-time monitoring result or applying for more server clusters to perform cloud rendering;
the parallel scheme determination is to perform corresponding parallelization processing on the cloud rendering task for completing the requirements of image quality and speed on the basis of two micro services of rendering image quality determination and rendering speed determination; the micro-service has three tasks, namely, available rendering calculation resources are monitored, an optimized rendering parallelism parameter is given on the basis of the first rendering cost, the rendering tasks are divided on the basis of the second rendering cost, and the principle of dividing is equivalent surface element equipartition, namely: for the dimension-reduced rear elements, under the background that the ray tracing force is similar and the rendering material basis is the same, the average division is realized, and the consistency of the rendering process is striven for; each decomposed graph automatically generates a thumbnail with a corresponding mark; according to the difference of rendering effects, rendering resources required by the decomposed graphic areas are different; determining the rendering resources required by each rendering area and the storage position of the rendering resources to form a parallel scheme;
the rendering resource allocation means that after a parallel scheme is formed, the micro-service firstly generates a refined execution scheme, secondly prepares and matches tasks and resources for a rendering server on the basis of the micro-service, completes the setting of corresponding rendering parameters, and loads the rendering resources into a rendering model according to the scheme;
the rendering resource allocation is to allocate rendering resources according to the set rendering parameters, and cloud rendering is started after the rendering resources are allocated;
the cloud rendering is to monitor the rendering process after the rendering resource loading is completed, realize the elastic circulation of tasks under the condition of exception, and form a rendering resource network after analyzing the rendering file; the cloud rendering server loads rendering resources to the rendering model according to the rendering resource network and then renders the rendering resources;
the rendering verification means that after the rendering is completed, the marked area after the rendering is compared with the thumbnail corresponding to the mark to check whether the rendering effect expected by the user is achieved or not;
the reconstruction of the rendering scheme refers to that after the cloud rendering task verification service is completed, if the rendering effect does not meet the requirement of a user, micro-service reconstruction is performed, a new rendering resource network is established, a new parallel scheme is formed, and rendering is performed again.
2. The cloud rendering service processing method using micro services according to claim 1, wherein: on the basis of the decomposition, standardized setting is carried out on each micro service, and the service content, the service quality, the service mode and the service sequence of the micro service are specified; after the standardized setting is performed, the cloud rendering server determines a connection mode, a combination mode and a torsion mechanism among the micro services according to the cloud rendering task, so that the rendering service can be automatically executed.
3. The cloud rendering service processing method using micro services according to claim 2, wherein: the torsion mechanism is realized by a service registration and service discovery mechanism; in the process, intelligent monitoring based on machine learning is used for monitoring each rendering action, and if an exception occurs, correction can be timely carried out.
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106101196A (en) * | 2016-06-01 | 2016-11-09 | 上海上大海润信息系统有限公司 | A kind of cloud rendering platform task scheduling system based on probabilistic model and method |
| CN106157355A (en) * | 2016-07-01 | 2016-11-23 | 国家超级计算深圳中心(深圳云计算中心) | A kind of fluid cloud based on high-performance calculation emulation rendering system and method |
| CN107862054A (en) * | 2017-11-09 | 2018-03-30 | 安徽教育网络出版有限公司 | A kind of pattern rendering intent |
| CN107930118A (en) * | 2017-12-28 | 2018-04-20 | 苏州蜗牛数字科技股份有限公司 | A kind of Unity engines post-process effect process visualization method and apparatus |
| CN108427581A (en) * | 2018-02-07 | 2018-08-21 | 平安普惠企业管理有限公司 | System micro services method and terminal device |
| CN108446111A (en) * | 2018-03-26 | 2018-08-24 | 国家电网公司客户服务中心 | A kind of micro services construction method based on Spring cloud |
| CN108648255A (en) * | 2018-05-10 | 2018-10-12 | 佛山欧神诺云商科技有限公司 | A kind of method and its device based on the self-defined rendering sample of asynchronous equalization |
| CN108701370A (en) * | 2016-03-10 | 2018-10-23 | 西门子保健有限责任公司 | The medical imaging based on content based on machine learning renders |
| CN108848092A (en) * | 2018-06-20 | 2018-11-20 | 中国联合网络通信集团有限公司 | The processing method and processing device of micro services gray scale publication based on call chain |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP5808424B2 (en) * | 2010-12-15 | 2015-11-10 | インターナショナル・ビジネス・マシーンズ・コーポレーションＩｎｔｅｒｎａｔｉｏｎａｌ Ｂｕｓｉｎｅｓｓ Ｍａｃｈｉｎｅｓ Ｃｏｒｐｏｒａｔｉｏｎ | Hardware-accelerated graphics for network-enabled applications |
| CA2831587C (en) * | 2013-02-06 | 2021-07-27 | Kabushiki Kaisha Square Enix Holdings (Also Trading As Square Enix Holdings Co., Ltd.) | Information processing apparatus, control method, program, and storage medium |
| US10659317B2 (en) * | 2016-03-31 | 2020-05-19 | Intel Corporation | Dynamically adapting cloud applications |
| US10600155B2 (en) * | 2017-02-09 | 2020-03-24 | Verizon Patent And Licensing Inc. | Generating virtual reality content based on corrections to stitching errors |
-
2018
- 2018-12-26 CN CN201811600459.XA patent/CN109671140B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108701370A (en) * | 2016-03-10 | 2018-10-23 | 西门子保健有限责任公司 | The medical imaging based on content based on machine learning renders |
| CN106101196A (en) * | 2016-06-01 | 2016-11-09 | 上海上大海润信息系统有限公司 | A kind of cloud rendering platform task scheduling system based on probabilistic model and method |
| CN106157355A (en) * | 2016-07-01 | 2016-11-23 | 国家超级计算深圳中心(深圳云计算中心) | A kind of fluid cloud based on high-performance calculation emulation rendering system and method |
| CN107862054A (en) * | 2017-11-09 | 2018-03-30 | 安徽教育网络出版有限公司 | A kind of pattern rendering intent |
| CN107930118A (en) * | 2017-12-28 | 2018-04-20 | 苏州蜗牛数字科技股份有限公司 | A kind of Unity engines post-process effect process visualization method and apparatus |
| CN108427581A (en) * | 2018-02-07 | 2018-08-21 | 平安普惠企业管理有限公司 | System micro services method and terminal device |
| CN108446111A (en) * | 2018-03-26 | 2018-08-24 | 国家电网公司客户服务中心 | A kind of micro services construction method based on Spring cloud |
| CN108648255A (en) * | 2018-05-10 | 2018-10-12 | 佛山欧神诺云商科技有限公司 | A kind of method and its device based on the self-defined rendering sample of asynchronous equalization |
| CN108848092A (en) * | 2018-06-20 | 2018-11-20 | 中国联合网络通信集团有限公司 | The processing method and processing device of micro services gray scale publication based on call chain |
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