CN111160810A - Workflow-based high-performance distributed spatial analysis task scheduling method and system - Google Patents
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Abstract
The invention relates to the technical field of workflow, in particular to a high-performance distributed space analysis task scheduling method and a system based on the workflow, which are different in that the method comprises the following steps: step 1), task splitting: receiving a user space analysis request, performing task analysis on the space analysis request to obtain a plurality of subtasks for space analysis, and adding all subtasks into a task pool of the request; step 2), task execution: obtaining all node lists of executable tasks, selecting an optimal node, selecting a subtask from a task pool and executing the subtask on the node; step 3), task merging: and after all the subtasks in the task pool are executed, merging all the subtasks. The invention divides the space analysis task which consumes a large amount of time and calculation into fine granularity, and quickly completes the space analysis task.
Description
Technical Field
The invention relates to the technical field of workflow, in particular to a high-performance distributed spatial analysis task scheduling method and system based on workflow.
Background
Workflow technology is a computational model of a workflow, i.e., logic and rules that organize how the work in the workflow is together in tandem are represented in a computer in an appropriate model and computed. Workflow is a concept proposed for regular activities with fixed programs in work. The aim of improving the production organization level and the working efficiency is achieved by decomposing the working activities into well-defined tasks, roles, rules and processes for execution and monitoring. The workflow technology provides advanced means for enterprises to better achieve business goals.
The workflow technology originated from research work in the field of office automation in the mid 70's of the 20 th century, but the workflow technology did not improve office efficiency greatly in the 1970's because the use of computers in offices was not generally accepted at that time, and developers were not aware of the needs and drawbacks of groupware technology. In 1993, the establishment of the international workflow management association (WfMC) marks the beginning of the workflow technology to enter a relatively mature stage, and is clearly divided into its own places in the field of computer application research. In order to realize the interoperation between different workflow products, the WfMC establishes a series of standards in terms of related terms, architecture, application programming interfaces and the like of a workflow management system. The workflow management alliance (WfMC) defines workflows as a class of business processes that can be fully or partially automated, and documents, information or tasks can be passed, executed between different actors according to a series of process rules. In practice, all processes whose execution is controlled by a computer software system (workflow management system) can be more broadly called workflows.
At present, a workflow is usually built in a system, and when a space analysis task is executed, the task cannot be split, so that time consumption is often high, the calculation amount is huge, and the analysis efficiency is low.
In view of the above, to overcome the above drawbacks, a method and a system for scheduling a high-performance distributed spatial analysis task based on a workflow are provided.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a workflow-based high-performance distributed spatial analysis task scheduling method and system, which are used for dividing a spatial analysis task which is time-consuming and large in calculation amount into fine granularities and quickly completing the spatial analysis task.
In order to solve the technical problems, the technical scheme of the invention is as follows: the workflow-based high-performance distributed spatial analysis task scheduling method is characterized by comprising the following steps of:
step 1), task splitting: receiving a user space analysis request, performing task analysis on the space analysis request to obtain a plurality of subtasks for space analysis, and adding all subtasks into a task pool of the request;
step 2), task execution: obtaining all node lists of executable tasks, selecting an optimal node, selecting a subtask from a task pool and executing the subtask on the node;
step 3), task merging: and after all the subtasks in the task pool are executed, merging all the subtasks.
According to the scheme, in the step 1), the task analysis is performed on the spatial analysis request according to the metadata information of the spatial analysis type.
According to the scheme, in the step 2), all the node lists capable of executing the task are obtained according to the metadata information of the current spatial analysis.
According to the scheme, in the step 2), the execution state of the task is monitored, and the efficiency and performance of the node for executing the task are recorded and used as a reference basis for next task scheduling.
According to the scheme, if the task fails in the task monitoring, the information of the current task execution failure is recorded, and the failed task is placed into the task pool again to wait for the task to be redistributed.
According to the scheme, in the step 3), the combination of the subtasks is also executed on each node.
The workflow-based high-performance distributed spatial analysis task scheduling system is characterized by comprising the following components:
a task splitting system: performing task analysis on the space analysis request to obtain a plurality of subtasks for space analysis, and adding all the subtasks into a task pool of the request; the task execution system: obtaining all node lists of executable tasks, selecting an optimal node, selecting a subtask from a task pool and executing the subtask on the node;
and a task merging system: and after all the subtasks in the task pool are executed, merging all the subtasks.
According to the scheme, the task execution system also comprises
The intelligent task scheduling system: when the space analysis subtasks are scheduled, the reliability and performance indexes of the nodes and the overall utilization rate of the cluster are comprehensively considered, and the space analysis is guaranteed to be reliable and an analysis result is obtained most quickly;
task execution state monitoring system: the state, progress and result of task execution are monitored in real time, and failure of space analysis caused by no response of a certain node and abnormity of network interruption is avoided;
task failover system: tasks are automatically and intelligently redistributed when subtasks fail.
The invention has the following beneficial effects:
1) easy to understand, development is simple:
according to the invention, only some interfaces are simply realized, a distributed program can be completed, the decomposed subtasks can be distributed to different interfaces for execution, and the task execution process is greatly simplified;
2) the expansibility of the system is good:
the number of the nodes can be increased according to the increase of the task amount of the processed data, so that the system can rapidly process offline data with huge data amount by increasing the number of the computing nodes to hundreds or thousands, and has good expansibility;
3) the method has high fault tolerance:
for subtasks which fail due to node faults, the system can automatically arrange the tasks to the nodes which normally run until the tasks are completed, so that the influence on the whole process of the tasks is small.
Drawings
FIG. 1 is a schematic flow chart of an embodiment of the present invention;
FIG. 2 is a schematic diagram of a task splitting flow according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a task execution flow according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Many aspects of the invention are better understood with reference to the following drawings. The components in the drawings are not necessarily to scale. Instead, emphasis is placed upon clearly illustrating the components of the present invention. Moreover, in the several views of the drawings, like reference numerals designate corresponding parts.
The word "exemplary" or "illustrative" as used herein means serving as an example, instance, or illustration. Any embodiment described herein as "exemplary" or "illustrative" is not necessarily to be construed as preferred or advantageous over other embodiments. All of the embodiments described below are exemplary embodiments provided to enable persons skilled in the art to make and use the examples of the disclosure and are not intended to limit the scope of the disclosure, which is defined by the claims. In other instances, well-known features and methods are described in detail so as not to obscure the invention. For purposes of the description herein, the terms "upper," "lower," "left," "right," "front," "rear," "vertical," "horizontal," and derivatives thereof shall relate to the invention as oriented in fig. 1. Furthermore, there is no intention to be bound by any expressed or implied theory presented in the preceding technical field, background, brief summary or the following detailed description. It is also to be understood that the specific devices and processes illustrated in the attached drawings, and described in the following specification are simply exemplary embodiments of the inventive concepts defined in the appended claims. Hence, specific dimensions and other physical characteristics relating to the embodiments disclosed herein are not to be considered as limiting, unless the claims expressly state otherwise.
Referring to fig. 1 to 3, the difference between the high-performance distributed spatial analysis task scheduling method based on workflow according to the present invention is that the method includes the following steps:
step 1), task splitting: receiving a user space analysis request, performing task analysis on the space analysis request to obtain a plurality of subtasks for space analysis, and adding all subtasks into a task pool of the request;
step 2), task execution: obtaining all node lists of executable tasks, selecting an optimal node according to an intelligent task scheduling system, selecting a subtask from a task pool and executing the subtask on the node;
step 3), task merging: and after all the subtasks in the task pool are executed, merging all the subtasks.
Specifically, in step 1), the task analysis is performed on the spatial analysis request according to the metadata information of the spatial analysis type.
Specifically, in step 2), all node lists capable of executing the task are obtained according to the metadata information of the current spatial analysis.
Specifically, in the step 2), the execution state of the task is monitored, and the efficiency and performance of the node for executing the task are recorded as a reference basis for next task scheduling.
Specifically, if a task fails in task monitoring, information of current task execution failure is recorded, and the failed task is placed into a task pool again to wait for task redistribution.
As shown in fig. 3, the task execution flow is as follows:
step 21), obtaining metadata information of current spatial analysis;
step 22), obtaining all node lists capable of executing tasks;
step 23), task scheduling, selecting an optimal node, selecting a subtask from the task pool and executing the subtask on the node; meanwhile, monitoring the execution state of the subtasks, recording the efficiency and performance of the nodes for executing the subtasks, and taking the efficiency and performance as a reference basis for next task scheduling;
step 24), monitoring whether the subtasks have faults or not, outputting subtask results if the subtasks do not have faults, and returning to the step 23 until all the subtasks in the task pool are executed; if a fault occurs, continuing to execute the next step 25;
step 25), recording information of subtask execution failure;
step 26), putting the subtask whose execution failed into the task pool again, and returning to step 22.
Specifically, in step 3), the merging of the subtasks is also performed on each node.
Specifically, the task merging process is similar to the task executing process.
The workflow-based high-performance distributed spatial analysis task scheduling system is characterized by comprising the following components:
a task splitting system: performing task analysis on the space analysis request to obtain a plurality of subtasks for space analysis, and adding all the subtasks into a task pool of the request; the task execution system: obtaining all node lists of executable tasks, selecting an optimal node, selecting a subtask from a task pool and executing the subtask on the node;
and a task merging system: and after all the subtasks in the task pool are executed, merging all the subtasks.
Specifically, the task execution system further includes
The intelligent task scheduling system: when the space analysis subtasks are scheduled, the intelligent task scheduling system comprehensively considers the reliability and performance indexes of the nodes and the overall utilization rate of the cluster, so that the space analysis is ensured to be reliable, and the analysis result is obtained at the fastest speed;
task execution state monitoring system: the space analysis is often a time-consuming calculation task, and the task execution state monitoring system monitors the state, progress and result of task execution in real time, so that the failure of space analysis caused by the abnormal condition that a certain node has no response and network interruption is avoided;
task failover system: the high-performance spatial analysis engine is a distributed engine, the conditions such as failure and the like of nodes in the execution process of subtasks cannot be avoided, and a task fault transfer system comprises: tasks are automatically and intelligently redistributed when subtasks fail.
According to the embodiment of the invention, a high-performance spatial analysis engine is designed, a large task is split and executed on a plurality of computing nodes in parallel, and finally the nodes are controlled to merge the execution results of all subtasks. The high-performance spatial analysis engine is a distributed spatial analysis engine integrated in an IGSS cluster manager, fully utilizes the performance of a plurality of nodes of IGS, uses an IGSS spatial analysis task scheduler to divide spatial analysis tasks which are time-consuming and large in calculation amount into fine granularity, executes spatial analysis subtasks on the plurality of IGS nodes in parallel, and finally merges and summarizes the results of each subtask after all tasks are executed, and generates the results of spatial analysis to return to a user. The system integrates an intelligent task scheduling system, a task execution state monitoring system and a task fault transfer system.
The foregoing is a more detailed description of the present invention that is presented in conjunction with specific embodiments, and the practice of the invention is not to be considered limited to those descriptions. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.
Claims (8)
1. A workflow-based high-performance distributed spatial analysis task scheduling method is characterized by comprising the following steps:
step 1), task splitting: receiving a user space analysis request, performing task analysis on the space analysis request to obtain a plurality of subtasks for space analysis, and adding all subtasks into a task pool of the request;
step 2), task execution: obtaining all node lists of executable tasks, selecting an optimal node, selecting a subtask from a task pool and executing the subtask on the node;
step 3), task merging: and after all the subtasks in the task pool are executed, merging all the subtasks.
2. The workflow-based high-performance distributed spatial analysis task scheduling method of claim 1, wherein: in the step 1), the task analysis is performed on the spatial analysis request according to the metadata information of the spatial analysis type.
3. The workflow-based high-performance distributed spatial analysis task scheduling method of claim 1, wherein: in the step 2), all node lists capable of executing the task are obtained according to the metadata information of the current spatial analysis.
4. The workflow-based high-performance distributed spatial analysis task scheduling method of claim 1, wherein: in the step 2), the execution state of the task is monitored, and the efficiency and performance of the node for executing the task are recorded and used as a reference basis for next task scheduling.
5. The workflow-based high-performance distributed spatial analysis task scheduling method of claim 4, wherein: if the task fails in the task monitoring, the information of the current task execution failure is recorded, and the failed task is placed into the task pool again to wait for the task to be redistributed.
6. The workflow-based high-performance distributed spatial analysis task scheduling method of claim 5, wherein: in the step 3), the combination of the subtasks is also performed on each node.
7. A workflow-based high-performance distributed spatial analysis task scheduling system is characterized by comprising:
a task splitting system: performing task analysis on the space analysis request to obtain a plurality of subtasks for space analysis, and adding all the subtasks into a task pool of the request; the task execution system: obtaining all node lists of executable tasks, selecting an optimal node, selecting a subtask from a task pool and executing the subtask on the node;
and a task merging system: and after all the subtasks in the task pool are executed, merging all the subtasks.
8. The workflow-based high performance distributed spatial analysis task scheduling system of claim 7 wherein: the task execution system further comprises
The intelligent task scheduling system: when the space analysis subtasks are scheduled, the reliability and performance indexes of the nodes and the overall utilization rate of the cluster are comprehensively considered, and the space analysis is guaranteed to be reliable and an analysis result is obtained most quickly;
task execution state monitoring system: the state, progress and result of task execution are monitored in real time, and failure of space analysis caused by no response of a certain node and abnormity of network interruption is avoided;
task failover system: tasks are automatically and intelligently redistributed when subtasks fail.
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Application publication date: 20200515 |