CN119166342A - Multi-task collaborative data processing method and system based on cloud computing - Google Patents
Multi-task collaborative data processing method and system based on cloud computing Download PDFInfo
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Abstract
The embodiment of the invention provides a method and a system for processing multitasking collaborative data based on cloud computing, and belongs to the technical field of cloud computing. The method comprises the steps of obtaining an initial task, decomposing the initial task to obtain a plurality of subtasks and task relationships among the subtasks, clustering the subtasks according to the task relationships to obtain a plurality of target tasks, determining a target optimization model according to the plurality of target tasks, determining target loads and target execution sequences corresponding to the target tasks according to the target optimization model, and processing target data corresponding to the target tasks according to the target loads and the target execution sequences to obtain target processing results corresponding to the target tasks. The problems that the resource waste or the task delay and the like possibly occur when the existing scheduler manages and distributes subtasks are solved, and the efficiency of task execution and the utilization rate of resources are improved.
Description
Technical Field
The invention relates to the technical field of cloud computing, in particular to a method and a system for processing multi-task collaborative data based on cloud computing.
Background
With development of cloud computing technology, the cloud computing platform provides powerful computing capability and storage resources, and can support large-scale data processing tasks. Meanwhile, the cloud computing platform also provides various tools and services, such as a scheduler, a distributed computing architecture, a data sharing and collaboration tool, elastic expansion and automation, data transmission and synchronization, real-time monitoring and optimization, security and authority management and the like, and can support the realization of multi-task collaborative data processing. However, when the existing scheduler manages and distributes subtasks, problems such as resource waste or task delay may occur, and problems that affect the performance and efficiency of the system may also occur.
Disclosure of Invention
The embodiment of the invention mainly aims to provide a multi-task cooperative data processing method and system based on cloud computing, and aims to solve the problems that resource waste or task delay and the like possibly occur when a dispatcher manages and distributes subtasks in the related technology.
In a first aspect, an embodiment of the present invention provides a method for processing multitasking collaborative data based on cloud computing, including:
obtaining an initial task, decomposing the initial task to obtain a plurality of subtasks and task relations among the subtasks;
clustering the subtasks according to the task relation to obtain a plurality of target tasks;
Determining a target optimization model according to a plurality of target tasks, and determining a target load and a target execution sequence corresponding to the target tasks according to the target optimization model;
and processing target data corresponding to the target task according to the target load and the target execution sequence to obtain a target processing result corresponding to the target task.
In a second aspect, an embodiment of the present invention provides a cloud computing-based multitasking collaborative data processing system, including:
the task decomposition module is used for obtaining an initial task, decomposing the initial task and obtaining a plurality of subtasks and task relations among the subtasks;
The task clustering module is used for clustering the subtasks according to the task relation to obtain a plurality of target tasks;
the task optimization module is used for determining a target optimization model according to a plurality of target tasks and determining target loads and target execution sequences corresponding to the target tasks according to the target optimization model;
and the task execution module is used for processing the target data corresponding to the target task according to the target load and the target execution sequence to obtain a target processing result corresponding to the target task.
In a third aspect, an embodiment of the present invention further provides a terminal device, where the terminal device includes a processor, a memory, a computer program stored on the memory and executable by the processor, and a data bus for implementing a connection communication between the processor and the memory, where the computer program, when executed by the processor, implements the steps of any of the cloud computing based multitasking collaborative data processing methods as provided in the present specification.
In a fourth aspect, an embodiment of the present invention further provides a storage medium, where the storage medium stores one or more programs, and the one or more programs are executable by one or more processors to implement the steps of any of the cloud computing-based multitasking collaborative data processing methods as provided in the present specification.
The embodiment of the invention provides a method and a system for processing multitasking collaborative data based on cloud computing, wherein the method comprises the following steps: decomposing the initial task into a plurality of subtasks makes the complex initial task easier to manage and execute. Each subtask has explicit goals and specific requirements to help refine the work schedule and to facilitate tracking and monitoring task progress. By this method, large complex tasks can be broken down into smaller, more easily controlled portions, making the overall task execution more efficient and controllable. Further, by analyzing the task relationships among the sub-tasks and clustering them, the related sub-tasks may be combined into multiple target tasks. The clustering process is beneficial to reducing interference among subtasks and enhancing coordination among the tasks, so that the overall working efficiency is improved. The process ensures that related tasks can be carried out under optimal conditions, thereby avoiding unnecessary conflicts between tasks and improving the organization and execution effects of the tasks. And establishing a target optimization model based on the plurality of target tasks. By means of the target optimization model, an optimal target load and target execution sequence can be determined for each target task. The optimization process not only helps to effectively allocate resources, but also avoids resource waste and potential bottleneck problems, and remarkably improves the operation efficiency of the system. The application of the optimization model ensures reasonable use of resources, so that the task execution process is smoother and more efficient. Finally, the target data is processed according to the optimized target load and the target execution sequence, so that the task can be more efficiently completed, the expected target processing result can be obtained, and the problems of resource waste, task delay and the like possibly faced by the existing scheduler in the process of managing and distributing the subtasks are effectively solved. Through scientific task decomposition, reasonable task clustering, accurate optimization model and efficient data processing, the efficiency of task execution and the utilization rate of resources are remarkably improved, and a more efficient and systematic solution is provided for the management of complex tasks.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a method for processing multi-task collaborative data based on cloud computing according to an embodiment of the present invention;
FIG. 2 is a schematic block diagram of a multi-task collaborative data processing system based on cloud computing according to an embodiment of the present invention;
fig. 3 is a schematic block diagram of a structure of a terminal device according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that some, but not all embodiments of the invention are described. 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 flow diagrams depicted in the figures are merely illustrative and not necessarily all of the elements and operations/steps are included or performed in the order described. For example, some operations/steps may be further divided, combined, or partially combined, so that the order of actual execution may be changed according to actual situations.
It is to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
The embodiment of the invention provides a method and a system for processing multitasking collaborative data based on cloud computing. The cloud computing-based multi-task cooperative data processing method can be applied to terminal equipment, wherein the terminal equipment can be electronic equipment such as a tablet personal computer, a notebook computer, a desktop computer, a personal digital assistant, wearable equipment and the like. The terminal device may be a server or a server cluster.
Some embodiments of the invention are described in detail below with reference to the accompanying drawings. The following embodiments and features of the embodiments may be combined with each other without conflict.
Referring to fig. 1, fig. 1 is a flow chart of a method for processing multi-task collaborative data based on cloud computing according to an embodiment of the present invention.
As shown in fig. 1, the method for processing the multitasking collaborative data based on cloud computing includes steps S101 to S104.
Step S101, an initial task is obtained, and the initial task is decomposed to obtain a plurality of subtasks and task relations among the subtasks.
Illustratively, cloud computing platforms provide powerful computing power and storage resources that can support large-scale data processing tasks. Meanwhile, the cloud computing platform also provides various tools and services, such as a scheduler, a distributed computing architecture, a data sharing and collaboration tool, elastic expansion and automation, data transmission and synchronization, real-time monitoring and optimization, security and authority management and the like, and can support the realization of multi-task collaborative data processing.
The method includes the steps that an initial task which needs to be processed in a cloud computing platform is uploaded to the cloud computing platform according to actual requirements, and then the initial task is obtained, so that the initial task is disassembled into a plurality of subtasks. During disassembly, the complexity and workload of the task need to be considered, and the task needs to be decomposed into smaller and more manageable parts.
For example, a task is split in terms of functions or operational steps to obtain multiple sub-tasks, and each sub-task is described in detail, including its goals, scope, required resources, and intended results. This helps to ensure that each subtask is explicit and specific.
Illustratively, correlations between subtasks are determined. This includes determining which subtasks need to be started after the completion of other subtasks, which subtasks can be performed in parallel, which subtasks have dependencies between themselves, and so on. For example, it is determined which subtasks are preceding tasks and which are subsequent tasks. The pre-tasks need to be completed before the start of the subsequent tasks. Or to identify which subtasks may be performed in parallel to improve overall efficiency. And determining the task relation among the subtasks according to the task dependency relation and the priority.
In some embodiments, the decomposing the initial task to obtain a plurality of subtasks and task relationships between the subtasks includes obtaining a first task structure and a first task function corresponding to the initial task, further decomposing the initial task to obtain a plurality of first intermediate tasks according to the first task structure and the first task function, predicting satisfaction of the first intermediate tasks by using a task satisfaction model to obtain first satisfaction corresponding to the first intermediate tasks, performing task decomposition on the first intermediate tasks according to the first satisfaction to obtain a plurality of subtasks, determining dependency relationships corresponding to the subtasks, and determining the task relationships between the subtasks according to the dependency relationships.
Illustratively, analyzing the first task structure of the initial task includes organizing the components, hierarchical relationships, and interiors of the initial task. For example, an initial task may be broken down into multiple main phases or modules. A first task function to be implemented for the initial task is determined. This involves identifying the specific operations or services required to complete the initial task, e.g., implementing function 1 followed by implementing function 2 on a function 1 basis.
The initial task is illustratively broken down into a plurality of first intermediate tasks according to a first task structure and a first task function of the initial task.
Illustratively, a suitable task satisfaction model (e.g., a regression model based on historical data, a machine learning model, etc.) is used to evaluate the satisfaction of the task. Thereby applying a task satisfaction model to make satisfaction predictions for each first intermediate task. Inputs to the task satisfaction model include, but are not limited to, task characteristics, requirements, expected results, etc., to output a first satisfaction corresponding to the first intermediate task. The first satisfaction may be a number between 1 and 10.
When the first satisfaction degree corresponding to each first intermediate task does not meet the preset satisfaction degree, performing task decomposition on the initial task again according to the first task structure and the first task function, further obtaining a plurality of first intermediate tasks again, further performing satisfaction degree prediction on the obtained plurality of first intermediate tasks again by using a task satisfaction model to obtain a plurality of new first satisfaction degrees, and determining the obtained first intermediate tasks as a plurality of subtasks obtained by disassembling the initial task until the obtained first satisfaction degrees of the plurality of first intermediate tasks meet the preset satisfaction degree.
Illustratively, dependencies between individual subtasks are analyzed. It is determined which sub-tasks need to be started after the completion of other sub-tasks and which can be performed in parallel. And creating a task relation graph or a network graph, and displaying the dependency relation and the execution sequence among the subtasks. This helps to visualize the execution flow of tasks and coordinate the work. Thereby determining the task relationship between the subtasks according to the dependency relationship.
Specifically, by decomposing an initial task, predicting task satisfaction, optimizing task disassembly and defining task relationships, scientificity and efficiency of task management are effectively improved, potential problems are reduced, and coordination and flexibility of task execution are enhanced. Thereby providing support for the subsequent increase of resource utilization and task execution efficiency.
In some embodiments, the task disassembly of the first intermediate task according to the first satisfaction degree to obtain a plurality of subtasks includes obtaining a second task structure and a second task function corresponding to the first intermediate task if the first satisfaction degree is smaller than a preset satisfaction degree, further performing task disassembly of the first intermediate task according to the second task structure and the second task function to obtain a plurality of second intermediate tasks, predicting the satisfaction degree of the second intermediate task by using the task satisfaction model to obtain a second satisfaction degree corresponding to the second intermediate task, continuing task disassembly of the second intermediate task according to the second satisfaction degree until the second satisfaction degree corresponding to the second intermediate task is greater than or equal to the preset satisfaction degree, obtaining a plurality of subtasks according to the first intermediate task if the first satisfaction degree is greater than or equal to the preset satisfaction degree, and obtaining a plurality of subtasks according to the first intermediate task if the second satisfaction degree is greater than or equal to the preset satisfaction degree.
The method includes the steps that a preset satisfaction degree is set according to historical experience or expert experience, when the first satisfaction degree is smaller than the preset satisfaction degree, a second task structure and a second task function corresponding to a first intermediate task are obtained, task decomposition is conducted on the first intermediate task according to the second task structure and the second task function, and a plurality of second intermediate tasks are obtained.
The task satisfaction model is used for predicting satisfaction degree of the second intermediate task, second satisfaction degree corresponding to the second intermediate task is obtained, comparison is carried out according to the second satisfaction degree and preset satisfaction degree, and when the second satisfaction degree is smaller than the preset satisfaction degree, decomposition is continued on the second intermediate task until the satisfaction degree of all intermediate tasks obtained through decomposition is larger than or equal to the preset satisfaction degree.
For example, if the first satisfaction is greater than or equal to the preset satisfaction, the decomposed first intermediate task is determined to be a plurality of subtasks. And if the second satisfaction is greater than or equal to the preset satisfaction, determining the first intermediate task and the second intermediate task, of which the satisfaction obtained by disassembling is greater than or equal to the preset satisfaction, as a plurality of subtasks. In other words, the plurality of subtasks are tasks having satisfaction greater than or equal to a preset satisfaction after the initial tasks are disassembled. That is, the satisfaction degree corresponding to each of the plurality of subtasks is greater than or equal to the preset satisfaction degree.
Specifically, through dynamic task decomposition and optimization based on satisfaction, high-quality completion of the subtasks is ensured, resource allocation is optimized, predictability and flexibility of the subtasks are improved, and coordination and systemicity of the subtasks are enhanced, so that scientificity of task management is improved, potential problems are effectively reduced, and overall working efficiency is improved.
And step S102, clustering the subtasks according to the task relation to obtain a plurality of target tasks.
By way of example, task relationships include dependencies, which are defined as determining which subtasks must be completed before other subtasks, where there are pre-and post-dependencies, and parallelism, which are defined as those subtasks that can be performed simultaneously, where there are no direct dependencies between the tasks and are independent of each other.
For example, two subtasks having an interdependence relationship are respectively determined as a first center and a second center, and then similarity calculation is performed on the subtasks in the parallel relationship and the first center and the second center by using an appropriate similarity measurement method (such as cosine similarity, euclidean distance, jaccard coefficient, etc.). For each subtask in the parallel relationship, calculating the similarity value between the subtask and the first center and the second center. And classifying the subtasks in the parallel relationship into class clusters with higher similarity according to the calculation result. Each subtask should be assigned to the cluster corresponding to the center point with the highest similarity. And forming a plurality of class clusters according to the similarity classification result. Each cluster includes subtasks having a higher similarity to the center point. Each cluster of classes is defined as a target task. Each target task comprises a group of sub-tasks which are similar in function and independent of each other, and the sub-tasks with high similarity are aggregated together. If different target tasks have a dependency relationship, different target tasks need to be executed in turn according to the dependency relationship.
In some embodiments, the clustering processing is performed on the subtasks according to the task relation to obtain a plurality of target tasks, and the clustering processing comprises the steps of obtaining task information corresponding to the subtasks, building a task execution diagram according to the task relation among the subtasks, dividing the execution sequence of the subtasks according to the task execution diagram to obtain corresponding task sets under the same execution sequence, calculating functional similarity and execution mode similarity between any two related tasks in the task sets according to the task information, determining target similarity between the two related tasks according to the functional similarity and the execution mode similarity, and performing task clustering on the task sets according to the target similarity to obtain the plurality of target tasks.
Illustratively, task information is obtained for each subtask, including, but not limited to, task name, task description, input/output, and the like. Task relationships between subtasks are determined, particularly dependencies (e.g., preceding and following tasks) and parallelism (e.g., tasks that may be performed simultaneously). Thereby creating a task execution graph according to the task relationships between the subtasks. In the task execution graph, each subtask is used as a node, and task relationships (such as dependency relationships) among the subtasks are used as edge connection nodes. Edges in the task execution graph represent dependency orders and the possibility of parallel execution, which helps determine the execution order of the tasks.
Illustratively, the execution order of the subtasks is determined using the dependency relationships and the parallel relationships in the task execution graph. Topology ordering and the like can be used to ensure that all dependencies are satisfied. Sub-tasks having the same execution order are grouped into task sets according to the determined execution order. Each task set represents a group of sub-tasks that are identical in order of execution.
The method comprises the steps of calculating the functional similarity and the execution mode similarity between any two related tasks in a task set according to task information, and obtaining the execution mode information, such as execution time, resource consumption, operation frequency and the like, of the related tasks from the task information. Task descriptions of related tasks are obtained from the task information, task descriptions of any two related tasks in the task set are compared, and a proper similarity measurement method (such as cosine similarity, jaccard coefficients and the like) is used for calculating the functional similarity between any two related tasks. And comparing the execution modes of any two related tasks, calculating the similarity in the aspects of execution time, resource consumption and the like, and obtaining the similarity of the execution modes between any two related tasks.
By way of example, the functional similarity and the execution mode similarity are combined through weighted average, the target similarity between any two related tasks is calculated, a similarity threshold is set, and whether any two related tasks belong to the same target task is judged, so that the related tasks with high similarity are gathered together to form a plurality of target tasks.
Specifically, by establishing a task execution graph, the dependency relationship and the parallel relationship between the subtasks can be clearly identified, so that task management becomes more visual and efficient. The illustrated method is helpful to define critical paths and potential bottlenecks, and ensures that tasks are executed in the correct order, so that delays and conflicts caused by improper task order are reduced, and the overall execution efficiency is improved. By calculating the functional similarity between related tasks, functionally similar tasks can be identified and aggregated together, thereby optimizing task allocation and resource allocation. This approach helps to improve the efficiency and consistency of task processing. By analyzing the execution mode of the task, the task with similar execution mode can be found, which is helpful for optimizing task scheduling, reducing repeated work and improving resource utilization rate. The target similarity between tasks can be accurately determined by combining the functional similarity and the execution mode similarity, so that resources are effectively allocated and managed through task clustering, reasonable allocation of the resources between related tasks is ensured, and resource waste and conflict are avoided.
And step 103, determining a target optimization model according to a plurality of target tasks, and determining target loads and target execution sequences corresponding to the target tasks according to the target optimization model.
Illustratively, optimization objectives of the objective optimization model are determined, such as minimizing total execution time, maximizing resource utilization, balancing load, etc., such that appropriate evaluation metrics are selected to measure achievement of the optimization objectives, such as task completion time, resource consumption, etc.
Illustratively, detailed information of each target task is obtained, including execution time of the task, resource requirements, and the like. Constraints in the environment corresponding to the execution of cloud computing, such as available resources, system load, network bandwidth, and the like, are collected. And selecting a proper modeling method according to the optimization target. For example, linear programming, integer programming, heuristic algorithms, etc. may be used. And converting the optimization targets and the constraint conditions into a mathematical model including the definition of the objective functions and the constraint conditions, thereby obtaining the target optimization model.
Illustratively, the load of each target task is calculated based on a target optimization model. The load may include computing requirements, memory usage, data traffic, etc. Therefore, by utilizing the result of the optimization model, how to distribute task loads is determined, so that balanced distribution of the loads is realized, and further, the target loads corresponding to the target tasks are obtained. For example, compute-intensive tasks are assigned to compute-intensive nodes, and storage-intensive tasks are assigned to memory-intensive nodes.
Illustratively, the order of execution of the target tasks is determined based on the dependencies of the target tasks. All pre-tasks are guaranteed to be completed before the subsequent tasks to avoid execution conflicts. An optimization model is used to formulate a target execution order for the target tasks. For example, high priority tasks are preferentially executed, or resource intensive tasks are executed when the system load is low. And according to the calculated target execution sequence, the tasks are arranged into specific execution periods or resource nodes. A scheduling algorithm is used to optimize the actual execution order of the tasks, ensuring that the tasks are scheduled.
And step S104, processing the target data corresponding to the target task according to the target load and the target execution sequence to obtain a target processing result corresponding to the target task.
Illustratively, the target tasks are executed one by one according to the determined execution order, and a respective target load is assigned to each target task. And ensuring that each target task is completed according to the set sequence and accords with the dependency relationship among the target tasks, so that target data is processed by using the target tasks under the target load, including operations such as calculation, analysis, conversion and the like, and a target processing result corresponding to the target tasks is obtained.
In some embodiments, the target task includes a first task and a second task, the target load includes a first load corresponding to the first task and a second load corresponding to the second task, the target processing result includes a first processing result corresponding to the first task and a second processing result corresponding to the second task, the target data corresponding to the target task is processed according to the target load and the target execution order, the target processing result corresponding to the target task is obtained, the target processing result includes determining a first message queue corresponding to the first task executed by the first load and a second message queue corresponding to the second task executed by the second load, performing data exchange through the first message queue and the second message queue, obtaining first related data requiring the second task in the first task executed by the first load and obtaining second related data requiring the first task in the second task executed by the second load, and obtaining second related data requiring the first task executed by the first task according to the first related data.
The target task at least comprises a first task and a second task, the target load at least comprises a first load corresponding to the first task and a second load corresponding to the second task, and the target processing result at least comprises a first processing result corresponding to the first task and a second processing result corresponding to the second task.
In an exemplary embodiment, a first message queue corresponding to a first load in executing a first task is determined, and a second message queue corresponding to a second load in executing a second task is determined, where the first message queue is used for processing data transmission required by the first load in executing the first task. The second message queue is used for processing data transmission required by the second load in the second task execution process.
Illustratively, the first message queue and the second message queue are configured to ensure that they can normally transmit and receive data. When the first task is executed, first related data of a needed second task are sent to a first message queue, and then the first related data are sent to a second message queue through the first message queue, so that after the first related data are obtained in the second task, the first related data are sent to the first task through the second message queue, and then a first processing result corresponding to the first task is obtained by executing the first task according to the first related data.
In an exemplary embodiment, when executing the second task, the second relevant data of the first task is sent to the second message queue, and then the second relevant data is sent to the first message queue through the second message queue, so that after the second relevant data is obtained in the first task, the second relevant data is sent to the second task through the first message queue, and then the second processing result corresponding to the second task is obtained by executing the second task according to the second relevant data.
Specifically, the message queue effectively manages the data dependence and execution sequence among tasks, so that errors caused by dependence problems are avoided, and the tasks are ensured to be executed according to the expected sequence. The message queue supports real-time data transmission, so that task processing can reflect the latest data state, and timeliness and relevance of results are improved.
In some embodiments, the method further comprises determining a target database, storing the target processing result to the target database, and performing access right setting on the target processing result to obtain target access right corresponding to the target processing result, so that a target user performs data query in the target database according to the target access right.
Illustratively, the type of the target database (e.g., relational database, noSQL database) is first specified. Thereby selecting an appropriate target database according to the requirements. This may include consideration of the functionality, compatibility, and extensibility of the database.
Illustratively, it is determined who needs to access the data (i.e., the target user) and the permission level (e.g., read-only, read-write permission, etc.) of the target user. The user roles and permissions are configured in the target database. And creating corresponding user roles according to the requirements of the database management system, and distributing required rights for each role. And associating the target user with the configured role to ensure that the target user can access the data in the target database according to the assigned authority. Thereby providing a query interface or tool for the target user to assist the target user in accessing and querying the data in the target database according to the rights.
For example, the identity of the cloud platform and access management services (such as AWS IAM and Google IAM) are used for controlling access rights of the target task and the target user to data and resources, so that data security is ensured. For example, when a certain target user needs to access a certain data source, an administrator can allocate a corresponding target access right for the target user through an IAM service, so that the security of data is ensured. The steps are automatically carried out on the cloud computing platform without manual intervention, so that efficient and reliable multi-task cooperative data processing is realized.
In some embodiments, the method further comprises the steps of obtaining task running time and resource utilization rate corresponding to the target task, determining task execution states corresponding to the target task according to the task running time and the resource utilization rate, and determining an exception handling strategy corresponding to the target task according to the task execution states.
Illustratively, the use of the monitoring tool tracks task runtime and resource utilization of the target task. Criteria and thresholds for the execution state of the target task are defined. For example, a normal range of task run times and a normal range of resource utilization are set. And comparing the actually collected task running time and resource utilization rate data with set standards and thresholds, and analyzing whether the task is in a normal range. And determining the task execution state according to the analysis result. The target task may be in a normal, warning or abnormal state. For example, a task running time outside of an expected range or abnormally high resource utilization may indicate that the task is problematic.
Illustratively, a corresponding exception handling policy is formulated based on the task execution state. Different task execution states may need different processing measures, and when the task execution state is a normal state, special processing is not needed, so that monitoring can be continued, when the task execution state is a warning state, further checking or optimizing measures such as task configuration adjustment, resource increase and the like are needed, and when the task execution state is an abnormal state, measures may need to be taken immediately, including restarting a task, notifying operation and maintenance personnel, performing fault removal and the like.
The determined exception handling policy is applied to the actual operation after the exception handling policy corresponding to the target task is obtained.
For example, the monitoring service (such as AWS CloudWatch and Google Stackdriver) provided by the cloud platform is used for monitoring the execution condition, the resource usage condition and the performance index of the task in real time to obtain the task running time and the resource utilization rate corresponding to the target task, for example, when the target running time of a certain target task exceeds a preset time, the monitoring service can send out an alarm to remind an administrator of processing abnormality.
Specifically, the task running time and the resource utilization rate of the target task are obtained, the task execution state of the target task is accurately determined, and a proper exception handling strategy is formulated and implemented according to the task execution state, so that the stability of the task and the normal running of a system are ensured.
The embodiment of the invention provides a method and a system for processing multitasking collaborative data based on cloud computing, wherein the method comprises the following steps: decomposing the initial task into a plurality of subtasks makes the complex initial task easier to manage and execute. Each subtask has explicit goals and specific requirements to help refine the work schedule and to facilitate tracking and monitoring task progress. By this method, large complex tasks can be broken down into smaller, more easily controlled portions, making the overall task execution more efficient and controllable. Further, by analyzing the task relationships among the sub-tasks and clustering them, the related sub-tasks may be combined into multiple target tasks. The clustering process is beneficial to reducing interference among subtasks and enhancing coordination among the tasks, so that the overall working efficiency is improved. The process ensures that related tasks can be carried out under optimal conditions, thereby avoiding unnecessary conflicts between tasks and improving the organization and execution effects of the tasks. And establishing a target optimization model based on the plurality of target tasks. By means of the target optimization model, an optimal target load and target execution sequence can be determined for each target task. The optimization process not only helps to effectively allocate resources, but also avoids resource waste and potential bottleneck problems, and remarkably improves the operation efficiency of the system. The application of the optimization model ensures reasonable use of resources, so that the task execution process is smoother and more efficient. Finally, the target data is processed according to the optimized target load and the target execution sequence, so that the task can be more efficiently completed, the expected target processing result can be obtained, and the problems of resource waste, task delay and the like possibly faced by the existing scheduler in the process of managing and distributing the subtasks are effectively solved. Through scientific task decomposition, reasonable task clustering, accurate optimization model and efficient data processing, the efficiency of task execution and the utilization rate of resources are remarkably improved, and a more efficient and systematic solution is provided for the management of complex tasks.
Referring to fig. 2, fig. 2 is a schematic diagram of a cloud computing-based multi-task collaborative data processing system 200, where the cloud computing-based multi-task collaborative data processing system 200 includes a task decomposition module 201, a task clustering module 202, a task optimization module 203, and a task execution module 204, where the task decomposition module 201 is configured to obtain an initial task and decompose the initial task to obtain a plurality of subtasks and task relationships between the subtasks, the task clustering module 202 is configured to perform clustering processing on the subtasks according to the task relationships to obtain a plurality of target tasks, the task optimization module 203 is configured to determine a target optimization model according to the plurality of target tasks and determine a target load and a target execution order corresponding to the target tasks according to the target optimization model, and the task execution module 204 is configured to process target data corresponding to the target tasks according to the target load and the target execution order to obtain a target processing result corresponding to the target tasks.
In some embodiments, the task decomposition module 201 performs, in the process of decomposing the initial task to obtain a plurality of subtasks and task relationships between the subtasks:
acquiring a first task structure and a first task function corresponding to the initial task, and further performing task decomposition on the initial task according to the first task structure and the first task function to acquire a plurality of first intermediate tasks;
performing satisfaction prediction on the first intermediate task by using a task satisfaction model to obtain first satisfaction corresponding to the first intermediate task;
performing task disassembly on the first intermediate task according to the first satisfaction degree to obtain a plurality of subtasks;
And determining the corresponding dependency relationship among a plurality of subtasks, and determining the task relationship among the subtasks according to the dependency relationship.
In some embodiments, the task decomposition module 201 performs, in the task disassembly of the first intermediate task according to the first satisfaction degree to obtain a plurality of subtasks, the following steps:
If the first satisfaction is smaller than the preset satisfaction, a second task structure and a second task function corresponding to the first intermediate task are obtained, and task decomposition is further carried out on the first intermediate task according to the second task structure and the second task function, so that a plurality of second intermediate tasks are obtained;
Performing satisfaction prediction on the second intermediate task by using the task satisfaction model to obtain second satisfaction corresponding to the second intermediate task;
task decomposition is continued on the second intermediate task according to the second satisfaction degree until the second satisfaction degree corresponding to the second intermediate task is greater than or equal to the preset satisfaction degree;
If the first satisfaction is greater than or equal to the preset satisfaction, acquiring a plurality of subtasks according to the first intermediate task;
And if the second satisfaction is greater than or equal to the preset satisfaction, acquiring a plurality of subtasks according to the first intermediate task and the second intermediate task.
In some embodiments, the task clustering module 202 performs, in the process of clustering the subtasks according to the task relationships to obtain a plurality of target tasks:
Task information corresponding to the subtasks is obtained, and a task execution diagram is built according to the task relation among the subtasks;
dividing the execution sequence of the subtasks according to the task execution graph to obtain corresponding task sets under the same execution sequence;
Calculating the functional similarity and the execution mode similarity between any two related tasks in the task set according to the task information;
Determining target similarity between two related tasks according to the functional similarity and the execution mode similarity;
and carrying out task clustering on the task set according to the target similarity to obtain a plurality of target tasks.
In some embodiments, the target task includes a first task and a second task, the target load includes a first load corresponding to the first task and a second load corresponding to the second task, the target processing result includes a first processing result corresponding to the first task and a second processing result corresponding to the second task, and the task execution module 204 executes, in the process of processing, according to the target load and the target execution order, target data corresponding to the target task, to obtain a target processing result corresponding to the target task:
Determining a first message queue corresponding to the first load in the process of executing the first task and a second message queue corresponding to the second load in the process of executing the second task;
Data exchange is carried out through the first message queue and the second message queue, so that first relevant data requiring the second task in the process of executing the first task and second relevant data requiring the first task in the process of executing the second task are obtained;
Executing the first task according to the first related data to obtain a first processing result corresponding to the first task;
And executing the second task according to the second related data to obtain a second processing result corresponding to the second task.
In some implementations, the cloud computing-based multitasking collaborative data processing system 200 also performs:
determining a target database, storing the target processing result into the target database, and setting access rights to the target processing result to obtain target access rights corresponding to the target processing result, so that a target user performs data query in the target database according to the target access rights.
In some implementations, the cloud computing-based multitasking collaborative data processing system 200 also performs:
Acquiring task running time and resource utilization rate corresponding to the target task;
Determining a task execution state corresponding to the target task according to the task running time and the resource utilization rate;
And determining an exception handling strategy corresponding to the target task according to the task execution state.
In some embodiments, the cloud computing-based multitasking collaborative data processing system 200 may be applied to a terminal device.
It should be noted that, for convenience and brevity of description, the specific working process of the above-described cloud computing-based multi-task collaborative data processing system 200 may refer to the corresponding process in the foregoing embodiment of the cloud computing-based multi-task collaborative data processing method, which is not described herein.
Referring to fig. 3, fig. 3 is a schematic block diagram of a structure of a terminal device according to an embodiment of the present invention.
As shown in fig. 3, the terminal device 300 comprises a processor 301 and a memory 302, the processor 301 and the memory 302 being connected by a bus 303, such as an I2C (Inter-INTEGRATED CIRCUIT) bus.
In particular, the processor 301 is used to provide computing and control capabilities, supporting the operation of the entire terminal device. The Processor 301 may be a central processing unit (Central Processing Unit, CPU), the Processor 301 may also be other general purpose processors, digital signal processors (DIGITAL SIGNAL Processor, DSP), application SPECIFIC INTEGRATED Circuit (ASIC), field-Programmable gate array (Field-Programmable GATEARRAY, FPGA) or other Programmable logic device, discrete gate or transistor logic device, discrete hardware components, or the like. Wherein the general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
Specifically, the Memory 302 may be a Flash chip, a Read-Only Memory (ROM) disk, an optical disk, a U-disk, a removable hard disk, or the like.
It will be appreciated by those skilled in the art that the structure shown in fig. 3 is merely a block diagram of a portion of the structure related to the embodiment of the present invention, and does not constitute a limitation of the terminal device to which the embodiment of the present invention is applied, and that a specific server may include more or less components than those shown in the drawings, or may combine some components, or have a different arrangement of components.
The processor is used for running a computer program stored in the memory, and realizing any one of the cloud computing-based multi-task cooperative data processing methods provided by the embodiment of the invention when the computer program is executed.
In an embodiment, the processor is configured to run a computer program stored in the memory and to implement the following steps when the computer program is executed:
obtaining an initial task, decomposing the initial task to obtain a plurality of subtasks and task relations among the subtasks;
clustering the subtasks according to the task relation to obtain a plurality of target tasks;
Determining a target optimization model according to a plurality of target tasks, and determining a target load and a target execution sequence corresponding to the target tasks according to the target optimization model;
and processing target data corresponding to the target task according to the target load and the target execution sequence to obtain a target processing result corresponding to the target task.
In some embodiments, the processor 301 performs, in the process of decomposing the initial task to obtain a plurality of subtasks and a task relationship between the subtasks:
acquiring a first task structure and a first task function corresponding to the initial task, and further performing task decomposition on the initial task according to the first task structure and the first task function to acquire a plurality of first intermediate tasks;
performing satisfaction prediction on the first intermediate task by using a task satisfaction model to obtain first satisfaction corresponding to the first intermediate task;
performing task disassembly on the first intermediate task according to the first satisfaction degree to obtain a plurality of subtasks;
And determining the corresponding dependency relationship among a plurality of subtasks, and determining the task relationship among the subtasks according to the dependency relationship.
In some embodiments, the processor 301 performs, during the task disassembly of the first intermediate task according to the first satisfaction degree to obtain a plurality of subtasks:
If the first satisfaction is smaller than the preset satisfaction, a second task structure and a second task function corresponding to the first intermediate task are obtained, and task decomposition is further carried out on the first intermediate task according to the second task structure and the second task function, so that a plurality of second intermediate tasks are obtained;
Performing satisfaction prediction on the second intermediate task by using the task satisfaction model to obtain second satisfaction corresponding to the second intermediate task;
task decomposition is continued on the second intermediate task according to the second satisfaction degree until the second satisfaction degree corresponding to the second intermediate task is greater than or equal to the preset satisfaction degree;
If the first satisfaction is greater than or equal to the preset satisfaction, acquiring a plurality of subtasks according to the first intermediate task;
And if the second satisfaction is greater than or equal to the preset satisfaction, acquiring a plurality of subtasks according to the first intermediate task and the second intermediate task.
In some embodiments, the processor 301 performs, in the clustering process on the subtasks according to the task relationship to obtain a plurality of target tasks:
Task information corresponding to the subtasks is obtained, and a task execution diagram is built according to the task relation among the subtasks;
dividing the execution sequence of the subtasks according to the task execution graph to obtain corresponding task sets under the same execution sequence;
Calculating the functional similarity and the execution mode similarity between any two related tasks in the task set according to the task information;
Determining target similarity between two related tasks according to the functional similarity and the execution mode similarity;
and carrying out task clustering on the task set according to the target similarity to obtain a plurality of target tasks.
In some embodiments, the target task includes a first task and a second task, the target load includes a first load corresponding to the first task and a second load corresponding to the second task, the target processing result includes a first processing result corresponding to the first task and a second processing result corresponding to the second task, and the processor 301 performs, in the processing the target data corresponding to the target task according to the target load and the target execution order, to obtain a target processing result corresponding to the target task, the processing steps include:
Determining a first message queue corresponding to the first load in the process of executing the first task and a second message queue corresponding to the second load in the process of executing the second task;
Data exchange is carried out through the first message queue and the second message queue, so that first relevant data requiring the second task in the process of executing the first task and second relevant data requiring the first task in the process of executing the second task are obtained;
Executing the first task according to the first related data to obtain a first processing result corresponding to the first task;
And executing the second task according to the second related data to obtain a second processing result corresponding to the second task.
In some implementations, the processor 301 further performs:
determining a target database, storing the target processing result into the target database, and setting access rights to the target processing result to obtain target access rights corresponding to the target processing result, so that a target user performs data query in the target database according to the target access rights.
In some implementations, the processor 301 further performs:
Acquiring task running time and resource utilization rate corresponding to the target task;
Determining a task execution state corresponding to the target task according to the task running time and the resource utilization rate;
And determining an exception handling strategy corresponding to the target task according to the task execution state.
It should be noted that, for convenience and brevity of description, a specific working process of the above-described terminal device may refer to a corresponding process in the foregoing embodiment of the method for processing multi-task collaborative data based on cloud computing, which is not described herein again.
The embodiment of the invention also provides a storage medium for computer readable storage, the storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of any cloud computing-based multi-task collaborative data processing method provided by the embodiment of the invention.
The storage medium may be an internal storage unit of the terminal device of the foregoing embodiment, for example, a hard disk or a memory of the terminal device. The storage medium may also be an external storage device of the terminal device, such as a plug-in hard disk provided on the terminal device, a smart memory card (SMART MEDIA CARD, SMC), a Secure Digital (SD) card, a flash memory card (FLASH CARD), or the like.
Those of ordinary skill in the art will appreciate that all or some of the steps, systems, functional modules/units in the apparatus, and methods disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof. In a hardware embodiment, the division between functional modules/units mentioned in the above description does not necessarily correspond to the division of physical components, for example, one physical component may have a plurality of functions, or one function or step may be cooperatively performed by several physical components. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as known to those skilled in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by a computer. Furthermore, as is well known to those of ordinary skill in the art, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media.
It should be understood that the term "and/or" as used in the present specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations. It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising one does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments. The present invention is not limited to the above embodiments, and various equivalent modifications and substitutions can be easily made by those skilled in the art within the scope of the present invention, and these modifications and substitutions are intended to be included in the scope of the present invention. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (10)
1. A method for processing multitasking collaborative data based on cloud computing, the method comprising:
obtaining an initial task, decomposing the initial task to obtain a plurality of subtasks and task relations among the subtasks;
clustering the subtasks according to the task relation to obtain a plurality of target tasks;
Determining a target optimization model according to a plurality of target tasks, and determining a target load and a target execution sequence corresponding to the target tasks according to the target optimization model;
and processing target data corresponding to the target task according to the target load and the target execution sequence to obtain a target processing result corresponding to the target task.
2. The method of claim 1, wherein decomposing the initial task to obtain a plurality of subtasks and task relationships between the subtasks comprises:
acquiring a first task structure and a first task function corresponding to the initial task, and further performing task decomposition on the initial task according to the first task structure and the first task function to acquire a plurality of first intermediate tasks;
performing satisfaction prediction on the first intermediate task by using a task satisfaction model to obtain first satisfaction corresponding to the first intermediate task;
performing task disassembly on the first intermediate task according to the first satisfaction degree to obtain a plurality of subtasks;
And determining the corresponding dependency relationship among a plurality of subtasks, and determining the task relationship among the subtasks according to the dependency relationship.
3. The method of claim 2, wherein performing task disassembly on the first intermediate task according to the first satisfaction degree to obtain a plurality of subtasks comprises:
If the first satisfaction is smaller than the preset satisfaction, a second task structure and a second task function corresponding to the first intermediate task are obtained, and task decomposition is further carried out on the first intermediate task according to the second task structure and the second task function, so that a plurality of second intermediate tasks are obtained;
Performing satisfaction prediction on the second intermediate task by using the task satisfaction model to obtain second satisfaction corresponding to the second intermediate task;
task decomposition is continued on the second intermediate task according to the second satisfaction degree until the second satisfaction degree corresponding to the second intermediate task is greater than or equal to the preset satisfaction degree;
If the first satisfaction is greater than or equal to the preset satisfaction, acquiring a plurality of subtasks according to the first intermediate task;
And if the second satisfaction is greater than or equal to the preset satisfaction, acquiring a plurality of subtasks according to the first intermediate task and the second intermediate task.
4. The method according to claim 1, wherein clustering the subtasks according to the task relationship to obtain a plurality of target tasks comprises:
Task information corresponding to the subtasks is obtained, and a task execution diagram is built according to the task relation among the subtasks;
dividing the execution sequence of the subtasks according to the task execution graph to obtain corresponding task sets under the same execution sequence;
Calculating the functional similarity and the execution mode similarity between any two related tasks in the task set according to the task information;
Determining target similarity between two related tasks according to the functional similarity and the execution mode similarity;
and carrying out task clustering on the task set according to the target similarity to obtain a plurality of target tasks.
5. The method according to claim 1, wherein the target task includes a first task and a second task, the target load includes a first load corresponding to the first task and a second load corresponding to the second task, the target processing result includes a first processing result corresponding to the first task and a second processing result corresponding to the second task, the processing the target data corresponding to the target task according to the target load and the target execution order, and obtaining the target processing result corresponding to the target task includes:
Determining a first message queue corresponding to the first load in the process of executing the first task and a second message queue corresponding to the second load in the process of executing the second task;
Data exchange is carried out through the first message queue and the second message queue, so that first relevant data requiring the second task in the process of executing the first task and second relevant data requiring the first task in the process of executing the second task are obtained;
Executing the first task according to the first related data to obtain a first processing result corresponding to the first task;
And executing the second task according to the second related data to obtain a second processing result corresponding to the second task.
6. The method according to any one of claims 1-5, further comprising:
determining a target database, storing the target processing result into the target database, and setting access rights to the target processing result to obtain target access rights corresponding to the target processing result, so that a target user performs data query in the target database according to the target access rights.
7. The method according to any one of claims 1-5, further comprising:
Acquiring task running time and resource utilization rate corresponding to the target task;
Determining a task execution state corresponding to the target task according to the task running time and the resource utilization rate;
And determining an exception handling strategy corresponding to the target task according to the task execution state.
8. A cloud computing-based multitasking collaborative data processing system, comprising:
the task decomposition module is used for obtaining an initial task, decomposing the initial task and obtaining a plurality of subtasks and task relations among the subtasks;
The task clustering module is used for clustering the subtasks according to the task relation to obtain a plurality of target tasks;
the task optimization module is used for determining a target optimization model according to a plurality of target tasks and determining target loads and target execution sequences corresponding to the target tasks according to the target optimization model;
and the task execution module is used for processing the target data corresponding to the target task according to the target load and the target execution sequence to obtain a target processing result corresponding to the target task.
9. A terminal device, characterized in that the terminal device comprises a processor and a memory;
the memory is used for storing a computer program;
the processor is configured to execute the computer program and implement the cloud computing-based multitasking collaborative data processing method as claimed in any one of claims 1 to 7 when the computer program is executed.
10. A computer storage medium for computer storage, wherein the computer storage medium stores one or more programs executable by one or more processors to implement the steps of the cloud computing-based multitasking collaborative data processing method of any of claims 1-7.
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