CN117579480A - Cloud distributed resource data model expansion and labeling method and system - Google Patents
Cloud distributed resource data model expansion and labeling method and system Download PDFInfo
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Abstract
The invention provides a cloud distributed resource data model expansion and labeling method and a cloud distributed resource data model expansion and labeling system. The method comprises the following steps: RDD mechanisms of Spark computing models of different cloud environments are used as carriers, and corresponding data models are expanded to mark computing tasks and data resources, so that RDD distributed on different cloud environments is marked and managed uniformly. The scheme provided by the invention initiates a unified labeling management and labeling method of the distributed resources under the inter-cloud computing condition, thereby supporting the effective identification and unified management of the inter-cloud heterogeneous computing tasks and providing basic capability guarantee for realizing inter-cloud heterogeneous computing joint scheduling interoperation of the inter-cloud computing.
Description
Technical Field
The invention belongs to the field of cloud computing, and particularly relates to a cloud distributed resource data model expansion and labeling method and system.
Background
Along with the popularization of cloud computing technology, diversified heterogeneous mixed cloud environments represented by public cloud, private cloud and edge cloud and corresponding application modes thereof are endless, and the formation and maturity of inter-cloud computing environments are marked. Under the inter-cloud computing condition, because various big data computing and storage systems are designed and deployed under the local single-cloud and single-cluster conditions, a unified management and identification method for inter-cloud distributed resources is needed to be provided, so that resources distributed on different cloud environments participating in computing can be effectively distinguished, identified and managed and controlled.
Meanwhile, due to the isomerism of different cloud environments and different computing storage models, corresponding resource identification and management methods are also quite different. In the process of cloud computing for mass data, because the bottleneck of the core is the network bandwidth of interconnection and intercommunication of different cloud environments, the key of cloud computing is to fully exert the principle of pushing computing to data, reduce the possibility of mass data migration between the cloud environments and heterogeneous clusters as much as possible, and exchange algorithms and related computing tasks instead. Therefore, when the joint computing schedule is developed for different cloud environments, how to uniformly identify and manage and control the environment dependence of the computing task and the computing intermediate result thereof is also an important problem to be solved.
Disclosure of Invention
In order to solve the technical problems, the invention provides a technical scheme of an inter-cloud distributed resource data model expansion and labeling method, so as to solve the technical problems.
The invention discloses a cloud distributed resource data model expansion and labeling method, which comprises the following steps:
step S1, submitting a calculation storage task of an original calculation or storage model to a job task;
s2, acquiring static resource parameters, static calculation control parameters and complete business data objects of the job task through analysis of the job task;
step S3, expanding the static resource parameters, the static calculation control parameters and the complete business data objects of the job task, namely marking, and generating a resource label;
s4, expanding a data model of the business data object by the resource tag to generate a tag description attribute object of the model; the resource label registration is completed through unified labeling management of the resource labels;
s5, filling the registered resource labels into the expanded data model, completing marking of the business data object and generating an expanded business data object;
and S6, carrying out dynamic task reconstruction on the extended service data object, introducing static resource parameters and static calculation control parameters of the job task, generating a new target calculation job task, and submitting to execution.
According to the method of the first aspect of the present invention, in said step S1, the calculation storage task of the original calculation or storage model is submitted as a job task in the form of an operator.
According to the method of the first aspect of the present invention, in the step S2, the method for obtaining the complete business data object by parsing the job task includes:
and dynamically analyzing and positioning the business data object of the job task by calling dynamic metadata management to obtain a complete business data object.
According to the method of the first aspect of the present invention, in the step S2, the parsing of the job task is implemented in the form of operator execution; the business data object is an elastic data object.
According to the method of the first aspect of the present invention, in the step S3, the method for expanding, i.e. marking, the static resource parameter, the static calculation control parameter and the complete business data object of the job task, and generating the resource label includes:
generating a resource ID based on the static resource parameter;
generating a task ID based on the static calculation control parameter;
and generating a resource tag by combining the resource ID and the task ID.
According to the method of the first aspect of the present invention, in said step S4, said resource tag registration is implemented in the form of an operator execution.
According to the method of the first aspect of the present invention, in said step S6, said dynamic task reconstruction is implemented in the form of an operator execution.
The second aspect of the invention discloses a cloud distributed resource data model expansion and labeling system, which comprises:
the first processing module is configured to submit a calculation storage task of an original calculation or storage model to be a job task;
the second processing module is configured to acquire static resource parameters, static calculation control parameters and complete business data objects of the job task through analysis of the job task;
the third processing module is configured to expand, i.e. mark, the static resource parameters, the static calculation control parameters and the complete business data objects of the job task to generate a resource label;
the fourth processing module is configured to generate a tag description attribute object of a model by expanding the data model of the business data object by the resource tag; the resource label registration is completed through unified labeling management of the resource labels;
the fifth processing module is configured to fill the registered resource tag into the expanded data model to finish marking the business data object and generate an expanded business data object;
and the sixth processing module is configured to reconstruct the dynamic task of the extended service data object, introduce the static resource parameters and the static calculation control parameters of the job task, generate a new target calculation job task and submit the execution.
A third aspect of the invention discloses an electronic device. The electronic device comprises a memory and a processor, wherein the memory stores a computer program, and the processor realizes the steps in the cloud distributed resource data model expanding and labeling method of any one of the first aspects of the disclosure when executing the computer program.
A fourth aspect of the invention discloses a computer-readable storage medium. The computer readable storage medium stores a computer program which, when executed by a processor, implements the steps in a cloud distributed resource data model expansion and labeling method according to any one of the first aspects of the present disclosure.
In summary, the scheme provided by the invention initiates a unified labeling management and labeling method of distributed resources under inter-cloud computing conditions, thereby supporting effective identification and unified management of inter-cloud heterogeneous computing tasks and providing basic capability guarantee for realizing inter-cloud heterogeneous computing joint scheduling interoperation of inter-cloud computing.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of a method for expanding and labeling an inter-cloud distributed resource data model according to an embodiment of the present invention;
FIG. 2 is a block diagram of an expansion and annotation system for an inter-cloud distributed resource data model according to an embodiment of the present invention;
fig. 3 is a block diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention discloses a cloud distributed resource data model expansion and labeling method. The most direct idea is to build a set of unified distributed resource tag system by utilizing a unified operator model and a dynamic metadata management technology for the interoperation of big data heterogeneous computing models, and then automatically mark each computing task and corresponding resource objects participating in inter-cloud computing through an active management mechanism of tags, so that the resources and the computing tasks can be identified, controlled and managed through relevant tags in different cloud environments.
After a unified tag system and a tag management mechanism are established, another problem to be solved is how to mark the heterogeneous computing model on the heterogeneous cloud environment. Because of the huge difference between different cloud environments and management mechanisms of corresponding computing environments, standard Spark computing resources are deployed (or dynamically applied) on different cloud environments, then a RDD (elastic data object) mechanism is utilized to expand a corresponding data (intermediate result of a computing task) model, and registration and labeling of corresponding resource labels are completed, so that corresponding labels can be labeled on corresponding RDDs, and unified management and representation of computing task objects are completed on the mechanism and capability by utilizing the RDDs. In order to support the unified identification and management of distributed resources under the inter-cloud computing condition, the invention designs a set of mechanism for actively generating a unified resource tag system, and a mechanism and an implementation method for realizing corresponding tag labeling application and unified management through expanding a computing intermediate result data model. The core of the invention is that a dynamic metadata processing method of a big data heterogeneous computing model is utilized to automatically construct a corresponding uniform resource tag; and then, utilizing RDD mechanisms of Spark computing models of different cloud environments as carriers, and marking computing tasks and data resources by expanding corresponding data models, so that the RDD distributed on different cloud environments is uniformly marked and managed. RDD mechanisms of Spark computing models of different cloud environments are used as carriers, and corresponding data models are expanded to mark computing tasks and data resources, so that RDD distributed on different cloud environments is marked and managed uniformly. Fig. 1 is a flowchart of a cloud distributed resource data model expansion and labeling method according to an embodiment of the present invention, as shown in fig. 1, where the method includes:
step S1, submitting a calculation storage task of an original calculation or storage model to a job task;
s2, acquiring static resource parameters, static calculation control parameters and complete business data objects of the job task through analysis of the job task;
step S3, expanding the static resource parameters, the static calculation control parameters and the complete business data objects of the job task, namely marking, and generating a resource label;
s4, expanding a data model of the business data object by the resource tag to generate a tag description attribute object of the model; the resource label registration is completed through unified labeling management of the resource labels;
s5, filling the registered resource labels into the expanded data model, completing marking of the business data object and generating an expanded business data object;
and S6, carrying out dynamic task reconstruction on the extended service data object, introducing static resource parameters and static calculation control parameters of the job task, generating a new target calculation job task, and submitting to execution.
In step S1, a calculation storage task of an original calculation or storage model is submitted as a job task.
In some embodiments, in said step S1, the calculation storage task of the original calculation or storage model is submitted in operator form as a job task.
In step S2, the static resource parameters, the static calculation control parameters and the complete business data object of the job task are obtained by analyzing the job task.
In some embodiments, in the step S2, the method for obtaining the complete business data object through parsing the job task includes:
and dynamically analyzing and positioning the business data object of the job task by calling dynamic metadata management to obtain a complete business data object.
The analysis of the job task is realized in the form of operator execution; the business data object is an elastic data object.
In step S3, expanding, i.e. marking, the static resource parameters, the static calculation control parameters and the complete business data object of the job task to generate a resource label.
In some embodiments, in the step S3, the method for expanding, i.e. marking, the static resource parameter, the static calculation control parameter, and the complete business data object of the job task, and generating a resource label includes:
generating a resource ID based on the static resource parameter;
generating a task ID based on the static calculation control parameter;
and generating a resource tag by combining the resource ID and the task ID.
In step S4, the resource tag expands the data model of the service data object to generate a tag description attribute object of the model; and finishing the registration of the resource tag by uniformly labeling and managing the resource tag.
In some embodiments, in the step S4, the resource tag registration is implemented in the form of an operator execution.
In step S6, the dynamic task reconstruction is performed on the extended service data object, the static resource parameters and the static calculation control parameters of the job task are introduced, a new target calculation job task is generated, and execution is submitted.
In some embodiments, in said step S6, said dynamic task reconstruction is implemented in the form of an operator execution.
In summary, the scheme provided by the invention initiates a unified labeling management and labeling method of distributed resources under inter-cloud computing conditions, thereby supporting effective identification and unified management of inter-cloud heterogeneous computing tasks and providing basic capability guarantee for realizing inter-cloud heterogeneous computing joint scheduling interoperation of inter-cloud computing. .
The invention discloses a cloud distributed resource data model expansion and labeling system. FIG. 2 is a block diagram of an expansion and annotation system for an inter-cloud distributed resource data model according to an embodiment of the present invention; as shown in fig. 2, the system 100 includes:
a first processing module 101 configured to submit a calculation storage task of an original calculation or storage model as a job task;
the second processing module 102 is configured to obtain the static resource parameters, the static calculation control parameters and the complete business data object of the job task through analyzing the job task;
the third processing module 103 is configured to expand, i.e. mark, the static resource parameters, the static calculation control parameters and the complete business data object of the job task to generate a resource label;
a fourth processing module 104 configured to generate a tag description attribute object of the model by expanding the data model of the service data object by the resource tag; the resource label registration is completed through unified labeling management of the resource labels;
a fifth processing module 105, configured to fill the registered resource tag with the expanded data model, complete labeling of the service data object, and generate an expanded service data object;
and a sixth processing module 106, configured to perform dynamic task reconstruction on the extended service data object, introduce the static resource parameters and the static calculation control parameters of the job task, generate a new target calculation job task, and submit execution.
According to the system of the second aspect of the present invention, the first processing module 101 is specifically configured to submit the calculation storage task of the original calculation or storage model as a job task in the form of an operator.
According to the system of the second aspect of the present invention, the second processing module 102 is specifically configured to obtain, through parsing the job task, a complete business data object, where the method includes:
and dynamically analyzing and positioning the business data object of the job task by calling dynamic metadata management to obtain a complete business data object.
The analysis of the job task is realized in the form of operator execution; the business data object is an elastic data object.
According to the system of the second aspect of the present invention, the third processing module 103 is specifically configured to expand, i.e. mark, the static resource parameters, the static calculation control parameters and the complete business data objects of the job task, and the method for generating the resource label includes:
generating a resource ID based on the static resource parameter;
generating a task ID based on the static calculation control parameter;
and generating a resource tag by combining the resource ID and the task ID.
According to the system of the second aspect of the present invention, the fourth processing module 104 is specifically configured such that the resource tag registration is implemented in the form of operator execution.
According to the system of the second aspect of the present invention, the sixth processing module 106 is specifically configured such that the dynamic task reconstruction is implemented in the form of operator execution.
A third aspect of the invention discloses an electronic device. The electronic equipment comprises a memory and a processor, wherein the memory stores a computer program, and the processor realizes the steps in the cloud distributed resource data model expansion and labeling method according to any one of the first aspect of the invention when executing the computer program.
Fig. 3 is a block diagram of an electronic device according to an embodiment of the present invention, and as shown in fig. 3, the electronic device includes a processor, a memory, a communication interface, a display screen, and an input device connected through a system bus. Wherein the processor of the electronic device is configured to provide computing and control capabilities. The memory of the electronic device includes a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The communication interface of the electronic device is used for conducting wired or wireless communication with an external terminal, and the wireless communication can be achieved through WIFI, an operator network, near Field Communication (NFC) or other technologies. The display screen of the electronic equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the electronic equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the electronic equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
Those skilled in the art will appreciate that the structure shown in fig. 3 is merely a structural diagram of a portion related to the technical solution of the present disclosure, and does not constitute a limitation of the electronic device to which the present application is applied, and that a specific electronic device may include more or less components than those shown in the drawings, or may combine some components, or have different component arrangements.
A fourth aspect of the invention discloses a computer-readable storage medium. The computer readable storage medium stores a computer program which, when executed by a processor, implements the steps in a cloud distributed resource data model expansion and labeling method according to any one of the first aspects of the disclosure.
Note that the technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be regarded as the scope of the description. The foregoing examples represent only a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the invention. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application is to be determined by the claims appended hereto.
Claims (10)
1. The cloud distributed resource data model expansion and labeling method is characterized by comprising the following steps of:
step S1, submitting a calculation storage task of an original calculation or storage model to a job task;
s2, acquiring static resource parameters, static calculation control parameters and complete business data objects of the job task through analysis of the job task;
step S3, expanding the static resource parameters, the static calculation control parameters and the complete business data objects of the job task, namely marking, and generating a resource label;
s4, expanding a data model of the business data object by the resource tag to generate a tag description attribute object of the model; the resource label registration is completed through unified labeling management of the resource labels;
s5, filling the registered resource labels into the expanded data model, completing marking of the business data object and generating an expanded business data object;
and S6, carrying out dynamic task reconstruction on the extended service data object, introducing static resource parameters and static calculation control parameters of the job task, generating a new target calculation job task, and submitting to execution.
2. The expansion and labeling method of cloud distributed resource data model according to claim 1, wherein in the step S1, the calculation storage task of the original calculation or storage model is submitted as a job task in the form of an operator.
3. The expansion and labeling method of cloud distributed resource data model according to claim 1, wherein in the step S2, the method for obtaining the complete business data object by analyzing the job task comprises:
and dynamically analyzing and positioning the business data object of the job task by calling dynamic metadata management to obtain a complete business data object.
4. The expansion and labeling method of cloud distributed resource data model according to claim 3, wherein in the step S2, the analysis of the job task is implemented in the form of operator execution; the business data object is an elastic data object.
5. The expansion and labeling method of cloud distributed resource data model according to claim 1, wherein in the step S3, the method for expanding, i.e. marking, the static resource parameters, the static calculation control parameters and the complete business data object of the job task, and generating the resource label includes:
generating a resource ID based on the static resource parameter;
generating a task ID based on the static calculation control parameter;
and generating a resource tag by combining the resource ID and the task ID.
6. The method for expanding and labeling a cloud distributed resource data model according to claim 1, wherein in the step S4, the resource tag registration is implemented in the form of operator execution.
7. The method for expanding and labeling a cloud distributed resource data model according to claim 1, wherein in the step S6, the dynamic task reconstruction is implemented in the form of operator execution.
8. A system for cloud distributed resource data model expansion and labeling, the system comprising:
the first processing module is configured to submit a calculation storage task of an original calculation or storage model to be a job task;
the second processing module is configured to acquire static resource parameters, static calculation control parameters and complete business data objects of the job task through analysis of the job task;
the third processing module is configured to expand, i.e. mark, the static resource parameters, the static calculation control parameters and the complete business data objects of the job task to generate a resource label;
the fourth processing module is configured to generate a tag description attribute object of a model by expanding the data model of the business data object by the resource tag; the resource label registration is completed through unified labeling management of the resource labels;
the fifth processing module is configured to fill the registered resource tag into the expanded data model to finish marking the business data object and generate an expanded business data object;
and the sixth processing module is configured to reconstruct the dynamic task of the extended service data object, introduce the static resource parameters and the static calculation control parameters of the job task, generate a new target calculation job task and submit the execution.
9. An electronic device, characterized in that the electronic device comprises a memory and a processor, the memory stores a computer program, and the processor implements the steps in a cloud distributed resource data model expansion and labeling method according to any one of claims 1 to 7 when executing the computer program.
10. A computer readable storage medium, wherein a computer program is stored on the computer readable storage medium, and when the computer program is executed by a processor, the steps in a cloud distributed resource data model expansion and labeling method according to any one of claims 1 to 7 are implemented.
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