CN112346841B - Service chain high-reliability tracing method based on block chain - Google Patents
Service chain high-reliability tracing method based on block chain Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 52
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- 230000003993 interaction Effects 0.000 claims abstract description 11
- 238000013461 design Methods 0.000 claims abstract description 4
- 238000013507 mapping Methods 0.000 claims abstract description 4
- 230000008447 perception Effects 0.000 claims abstract description 4
- 239000012634 fragment Substances 0.000 claims description 49
- 235000019580 granularity Nutrition 0.000 claims description 6
- 230000007246 mechanism Effects 0.000 claims description 6
- 238000004891 communication Methods 0.000 claims description 4
- 238000012790 confirmation Methods 0.000 claims description 3
- 238000013467 fragmentation Methods 0.000 claims description 3
- 238000006062 fragmentation reaction Methods 0.000 claims description 3
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Classifications
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/46—Multiprogramming arrangements
- G06F9/48—Program initiating; Program switching, e.g. by interrupt
- G06F9/4806—Task transfer initiation or dispatching
- G06F9/4843—Task transfer initiation or dispatching by program, e.g. task dispatcher, supervisor, operating system
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/46—Multiprogramming arrangements
- G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
- G06F9/5061—Partitioning or combining of resources
- G06F9/5072—Grid computing
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/46—Multiprogramming arrangements
- G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
- G06F9/5061—Partitioning or combining of resources
- G06F9/5077—Logical partitioning of resources; Management or configuration of virtualized resources
Abstract
The invention discloses a service chain high reliability tracing method based on a block chain, which is characterized in that: a three-layer architecture system is constructed: the bottom layer is used for finishing perception and interaction; middle layer to realize collection and management; high-level, realize deployment and application; four steps of order flow system, flow consultation system, result label system and label mapping system are established; in the above steps based on blockchain implementation, design is made around reliability; the order flow system adopts a user credible cut-in method; the process is manufactured by a business, and a reliable interaction method is adopted; and (5) as a result, label making adopts a reliable slicing storage method. The high-reliability traceability method for the service chain based on the block chain greatly reduces the requirements of the system on storage space and storage resources; the data loss when faults occur is reduced, and more flexible data availability guarantee is provided; the reliability and stability of the rendering system service are improved.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a service chain high-reliability tracing method based on a block chain.
Background
In the historical wave of cloud computing tumbling, the cloud rendering mode also gradually sloughs, undergoing a transition from private cloud to hybrid cloud. The private cloud stage is limited in terms of computing node configuration, scale number and equipment updating, and although higher operation and maintenance cost is paid, the demands of market and enterprise users cannot be flexibly met; starting in 2016, part of rendering farms start to cooperate with public clouds, metamorphism enters a 'hybrid cloud' stage, the deficiency of an internal infrastructure is supplemented by utilizing the resource allocation of the public clouds, the overall load capacity and the operation efficiency are greatly improved, the operation cost is reduced, the infrastructure becomes more expandable, and more intelligent and efficient cloud rendering services are provided for users. For a rendering farm, the whole deployment of the public cloud means supporting the dynamic expansion of mass nodes, and the new public cloud architecture has the advantages of cost control, dynamic adjustment of rendering task queues and the like, and can provide flexible, efficient and massive rendering services for users quickly and conveniently. As a professional rendering service company, considering the current situation of rapid development of the rendering industry and the update iteration rate of computer hardware resources, public cloud rendering is a trend;
at present, a service flow from the rendering task submitting to the rendering result obtaining of a user is basically full-automatic in a main stream cloud rendering platform in China. The cloud rendering platform is provided with a special client, different rendering plug-ins are embedded in the special client, a user selects a corresponding submitting panel according to local plug-in configuration, the rendering plug-ins can automatically acquire and analyze information of a current scene and display the information in the submitting panel, the user sets rendering parameters according to actual rendering requirements and submits rendering, and a rendering result can be automatically pushed to the user after task rendering is completed; the user can manage the submitted rendering tasks at the client, including live preview, cancel rendering, etc.
The service chain included in the service flow from the rendering task submitting to the rendering result obtaining by the user mainly comprises the following steps: 1) Submitting a rendering order by a user; 2) The rendering platform schedules rendering nodes (multi-public cloud); 3) The rendering node completes rendering calculation according to the order parameters; 4) And returning the order rendering result to the local of the user.
With the evolution of the development pattern of public cloud service providers, rendering service companies are expected to seek a wider development space, so that the platform has advantages in the aspects of technical support and market competition, the leading position of a project platform is ensured, and the project platform cannot depend on a public cloud service provider of a certain furniture excessively; the main stream cloud rendering platform in China basically cooperates with a plurality of public cloud service providers, such as: arian cloud, hua Yuan Yun, beijing Dong Yun, amazon cloud (AWS), etc. Although rendering service companies eventually develop "public cloud" rendering, there are differences in communication protocols, service patterns, geographic locations, charging forms, discount rates, etc. of different public clouds, thereby forming another form of "hybrid cloud" of multiple operators. Before the rendering platform uploads the rendering job, the rendering platform needs to monitor and evaluate the factors such as the quantity, the scale, the configuration, the availability and the like of the computing resources in each geographic position, dynamically decides to dispatch the rendering job to the computing resources in each geographic position, and can still provide high-availability cloud rendering service through the available computing resources in other geographic positions when one geographic position fails.
Because of the difference between different public cloud service providers, the data flows among the service chains of the current cloud rendering service flow are independent of each other, a reliable mechanism is lacked to record and trace the data formed by each service chain, once the rendering result is problematic, it is difficult to determine which service chain is problematic, and the problem cannot be rapidly examined and solved, so that the efficiency of the whole rendering service flow is reduced, and the rendering experience of a user is reduced.
Disclosure of Invention
The invention aims to solve the problems that responsibility and service tracing reliability are low when the quality of a product cannot be determined in the rendering process of the existing cloud rendering service, and provides a service chain high-reliability tracing method based on a block chain.
In order to achieve the aim of the invention, the invention adopts the following technical scheme that the service chain is traced with high reliability: a service chain high reliability tracing method based on a block chain constructs a three-layer architecture system: the bottom layer is used for finishing perception and interaction; middle layer to realize collection and management; high-level, realize deployment and application. In the concrete operation, four steps of order flow system, flow consultation system, result label system and label mapping system are established. In the above mechanism based on blockchain implementation, a design is made around reliability. The order flow system adopts a user credible cut-in method; the process is manufactured by a business, and a reliable interaction method is adopted; and (5) as a result, label making adopts a reliable slicing storage method.
The method comprises the following specific steps:
the first step, an order flow system adopting a user trusted cut-in method is provided, credit of a user is used as guarantee of transaction, false rendering tasks are distinguished based on a rendering system data center, and resource files which do not need to be processed are filtered. Based on a service chain system platform, in the whole service chain tracing process, data are stored in a block chain, and information interaction between a user and the cloud is realized through a platform business module.
The method comprises the following steps:
1. the platform evaluates the value of rendering resources submitted by users, establishes a credit system, and defines the value name as a credit value;
2. and pre-judging the threshold value of the resource in the pre-processing process. The use amount is the characteristic quantity extracted by the rendering resource, and the characteristic quantity of 10-80% is regarded as a real rendering task, is a threshold value which can be successfully transacted, is a false rendering task which is lower than or exceeds the threshold value, and refuses the rendering request of a user.
3. The user credits may be accumulated and the credit value may be calculated in the following manner: new credit = credit + accumulated credit;
4. in the service chain, the system may determine an upper credit limit. The system defines the interval of the credit fee to select which transactions are packed, and the selected transaction information is sent to the next node;
the second step, adopting a reliable interactive flow consultation system, realizing consultation among different clouds by utilizing push-pull, and coordinating data communication among different clouds by a cloud management center, comprising the following steps:
1. after the push-pull middle cloud processes the resources, the resources are requested to be pushed out, other clouds continuously monitor information about the clouds in the background, and after the information about the clouds is monitored, the resources are requested to be pulled into the clouds;
2. after the cloud sends out the request, the output signal 1 requests that the resource can be deduced, and when other clouds simultaneously send out the request, the input signal 1 requests that the resource can be pulled, the capacity and the requirement of the resource in a plurality of clouds are matched after the confirmation mechanism agrees, and successful matching is authorized. After the matching is successful, pushing out and pulling the resource to the cloud with successful request, feeding back the result to the cloud, and sending the result to the output signal 1 when pushing the request;
3. in order to avoid congestion caused by network resources to be processed and waste a great amount of time, the cloud processing center platform distributes the resources to be processed which are accumulated to 1-2 granularities to different service layers of different clouds;
and thirdly, adopting a result label manufacturing method of a reliable fragment storage method to store the resource fragments, wherein the method comprises the following steps:
1. the cloud processing platform determines the size of the fragments according to the load balancing state, the smaller the fragments are, the higher the balancing load degree is,
when the fragments fail, only the failed fragments are processed, other fragments are not affected, and the total data loss is reduced;
2. the reliability of the fragments is reflected in that the granularity of the backup fragments is reduced after the fragments are blocked, the backup speed is increased, and the fragments can be quickly found in the backup fragments after the fragments are blocked;
3. according to the quick positioning and searching of the labels, the labels are classified into service classification, user classification, product classification and the like, and rendering resource fragments are stored as different labels;
4. the fragment tag comprises 16 bytes, is expressed by decimal, and comprises 1-4 bits as fragment ID,5-8 bits as storage resource addressing address, 9-12 bits as resource size and 13-16 bits as fragment length.
The invention has the beneficial effects that:
the high-reliability traceability method for the service chain based on the block chain greatly reduces the requirements of the system on storage space and storage resources; the data loss when faults occur is reduced, and more flexible data availability guarantee is provided; the reliability and stability of the rendering system service are improved.
Drawings
FIG. 1 is a schematic diagram of a credit architecture of an embodiment of the invention.
Fig. 2 is a schematic push-pull diagram of an embodiment of the present invention.
FIG. 3 is a schematic diagram of a tiled memory according to an embodiment of the present invention.
FIG. 4 is a schematic diagram of a flow consultation according to an embodiment of the present invention.
Detailed Description
The invention is described in further detail below with reference to the drawings and the specific examples.
In order to realize high reliability tracing of the service chain, the high reliability tracing method of the service chain based on the blockchain in the embodiment constructs a three-layer architecture system: the bottom layer is used for finishing perception and interaction; middle layer to realize collection and management; high-level, realize deployment and application. In the concrete operation, four steps of order flow system, flow consultation system, result label system and label mapping system are established. In the above mechanism based on blockchain implementation, a design is made around reliability. The order flow system adopts a user credible cut-in method; the process is manufactured by a business, and a reliable interaction method is adopted; and (5) as a result, label making adopts a reliable slicing storage method. As shown in fig. 1, a credit architecture diagram of an embodiment of the present invention is shown.
The embodiment provides a service chain high reliability tracing method based on a block chain, and firstly provides an order flow system adopting a user trusted cut-in method, wherein credit of a user is used as a guarantee of transaction, false rendering tasks are distinguished based on a rendering system data center station, and resource files which do not need to be processed are filtered. Based on a service chain system platform, in the whole service chain tracing process, data are stored in a block chain, and information interaction between a user and the cloud is realized through a platform business module.
The method comprises the following steps:
(1) The platform evaluates the value of rendering resources submitted by users, establishes a credit system, and defines the value name as a credit value;
(2) And pre-judging the threshold value of the resource in the pre-processing process. The use amount is the characteristic quantity extracted by the rendering resource, and the characteristic quantity of 10-80% is regarded as a real rendering task, is a threshold value which can be successfully transacted, is a false rendering task which is lower than or exceeds the threshold value, and refuses the rendering request of a user.
(3) The user credits may be accumulated and the credit value may be calculated in the following manner: new credit = credit + accumulated credit;
(4) In the service chain, the system may determine an upper credit limit. The system defines the interval of the credit fee to select which transactions are packed, and the selected transaction information is sent to the next node.
As shown in fig. 4, a schematic diagram of a flow consultation according to an embodiment of the present invention is shown. The second step of the service chain high reliability tracing method based on the block chain is to adopt a reliable interactive process consultation system and realize consultation among different clouds by utilizing push-pull, as shown in fig. 2, and the method is a push-pull schematic diagram in the embodiment of the invention. The cloud management center coordinates data communication among different clouds, and comprises the following steps:
(1) After the push-pull middle cloud processes the resources, the resources are requested to be pushed out, other clouds continuously monitor information about the clouds in the background, and after the information about the clouds is monitored, the resources are requested to be pulled into the clouds;
(2) After the cloud sends out the request, the output signal 1 requests that the resource can be deduced, and when other clouds simultaneously send out the request, the input signal 1 requests that the resource can be pulled, the capacity and the requirement of the resource in a plurality of clouds are matched after the confirmation mechanism agrees, and successful matching is authorized. After the matching is successful, pushing out and pulling the resource to the cloud with successful request, feeding back the result to the cloud, and sending the result to the output signal 1 when pushing the request;
(3) In order to avoid congestion caused by network resources to be processed and waste a great deal of time, the cloud processing center platform distributes the resources to be processed which are accumulated to 1-2 granularities to different service layers of different clouds.
As shown in fig. 3, a tiled memory schematic of an embodiment of the present invention. The third step, adopting the result label of the reliable fragmentation storage method to store the resource fragments, comprises the following steps:
1. the cloud processing platform determines the size of the fragments according to the load balancing state, the smaller the fragments are, the higher the balancing load degree is, when the fragments are in failure, only the failure fragments are required to be processed, other fragments are not affected, and the total data loss is reduced;
2. the reliability of the fragments is reflected in that the granularity of the backup fragments is reduced after the fragments are blocked, the backup speed is increased, and the fragments can be quickly found in the backup fragments after the fragments are blocked;
3. according to the quick positioning and searching of the labels, the labels are classified into service classification, user classification, product classification and the like, and rendering resource fragments are stored as different labels;
4. the fragment tag comprises 16 bytes, is expressed by decimal, and comprises 1-4 bits as fragment ID,5-8 bits as storage resource addressing address, 9-12 bits as resource size and 13-16 bits as fragment length.
While the invention has been described in terms of preferred embodiments, the embodiments and drawings are not intended to limit the invention, but rather, it will be apparent to those skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope of the invention. The scope of the invention should, therefore, be determined with reference to the appended claims.
Claims (1)
1. A service chain high reliability tracing method based on a block chain constructs a three-layer architecture system: the bottom layer is used for finishing perception and interaction; middle layer to realize collection and management; high-level, realize deployment and application; four steps of order flow system, flow consultation system, result label system and label mapping system are established; in the above steps based on blockchain implementation, design is made around reliability; the order flow system adopts a user credible cut-in method; the process is manufactured by a business, and a reliable interaction method is adopted; the result label is manufactured by adopting a reliable slicing storage method; the method is characterized by comprising the following specific steps of:
the method comprises the steps of providing an order flow system adopting a user trusted cut-in method, taking the credit of a user as a guarantee of transaction, and filtering out resource files which do not need to be processed based on a false rendering task resolved by a rendering system data center; based on a service chain system platform, in the whole service chain tracing process, data are stored in a block chain, and information interaction between a user and the cloud is realized through a platform business module;
the detailed flow is as follows:
step 1-1, a platform evaluates the value of rendering resources submitted by a user, establishes a credit system, and defines a value name as a credit value;
step 1-2, prejudging a threshold value of a resource in a pretreatment process; the usage amount is the characteristic quantity extracted by the rendering resource, and the characteristic quantity which is 10% -80% is regarded as a real rendering task, is a threshold value which can be successfully transacted, is a false rendering task which is lower than or exceeds the threshold value, and refuses the rendering request of a user;
step 1-3, user credit can be accumulated, and the credit value is calculated in the following way: new credit = credit + accumulated credit;
step 1-4, in the service chain, the system can determine the upper limit of the credit value, the system defines the interval of the credit fee to select which transactions are packed, and the selected transaction information is sent to the next node;
secondly, adopting a reliable interactive flow consultation system, realizing consultation among different clouds by utilizing push-pull, and coordinating data communication among different clouds by a cloud management center;
the specific flow is as follows:
step 2-1, after the push-pull type middle cloud processes the resources, requesting to push out the resources, and after the information about the cloud is monitored, requesting to pull the resources into the cloud by other clouds in the background;
step 2-2, after the cloud sends out the request, outputting a signal, namely a request, and pushing out the resource, and when other clouds simultaneously send out the request, inputting the signal, namely the request, and pulling out the resource, wherein the resource in a plurality of clouds is required to match the capacity and the requirement after the confirmation mechanism agrees, and the successful matching is authorized; after the matching is successful, pushing out and pulling the resource to the cloud with successful request, feeding back the result to the cloud, and outputting a first signal when the result is sent to the pushing request;
step 2-3, in order to avoid congestion caused by network resources to be processed and waste a great deal of time, the cloud processing center platform distributes the resources to be processed which are accumulated to 1-2 granularities to different service layers of different clouds;
thirdly, adopting a result label of a reliable fragmentation storage method to manufacture and store the resource fragmentation;
the specific flow is as follows:
step 3-1, the cloud processing platform determines the size of the fragments according to the load balancing state, the smaller the fragments are, the higher the balancing load degree is, when the fragments are in failure, only the failure fragments are required to be processed, other fragments are not affected, and the total data loss is reduced;
step 3-2, the reliability of the fragments is reflected in that after the fragments are segmented, the granularity of the backup fragments is reduced, the backup speed is increased, and the fragments can be quickly found in the backup fragments after the fragments are blocked;
step 3-3, quickly positioning and searching fragments according to labels, wherein the labels are classified into service classification, user classification, product classification and the like, and rendering resource fragments are stored as different labels;
step 3-4, the fragment tag contains 16 bytes, and is expressed by decimal system, wherein 1-4 bits are fragment ID,5-8 bits are storage resource addressing address, 9-12 bits are resource size, and 13-16 bits are fragment length.
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