CN111199316A - Cloud and mist collaborative computing power grid scheduling method based on execution time evaluation - Google Patents
Cloud and mist collaborative computing power grid scheduling method based on execution time evaluation Download PDFInfo
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Abstract
The invention relates to a cloud and mist collaborative computing power grid scheduling method based on execution time evaluation, which comprises the following steps: step S1: evaluating task execution time; step S2: judging the type of a scheduling task; step S3: and carrying out task scheduling based on the task scheduling optimization method of the improved genetic-ant colony algorithm. According to the method, a hadoop technology, a cloud computing technology and a fog computing technology are fused, so that the execution time of a scheduling task is firstly evaluated when the intelligent power grid scheduling system faces the task submitted by a user, whether the task belongs to an emergency task or a non-emergency task is judged, the task is scheduled to a corresponding platform according to a judgment result to execute a corresponding scheduling strategy, optimal scheduling is achieved, resource allocation is reasonable and flexible, task scheduling efficiency is guaranteed, the utilization rate of resources is improved, and stability and reliability of power grid scheduling are guaranteed.
Description
[ technical field ] A method for producing a semiconductor device
The invention belongs to the technical field of power grid dispatching automation, and particularly relates to a cloud and mist collaborative computing power grid dispatching method based on execution time evaluation.
[ background of the invention ]
With the development of new IT technologies such as Internet of things, cloud computing, big data, artificial intelligence and the like, the electric power information data is explosively increased, so that the network bandwidth load is rapidly increased, data is blocked on the network, the data cannot be transmitted from a source end to the cloud end in real time, the data cannot be effectively uploaded to a cloud end server in time, and the optimal processing time of abnormal events is delayed, so that the simple cloud computing cannot meet the scheduling requirements of some tasks, the power grid scheduling control technology in China also needs to continuously absorb new technologies with time to consolidate and improve the supporting capability of information sensing and synchronization, real-time online analysis, scheduling lean management and data deep application of a power grid scheduling system, complete the task that the power grid scheduling operation mode is gradually changed from 'analytic scheduling' into 'intelligent scheduling', and the cloud and fog collaborative computing can well solve the problems, the functions of adaptive adjustment of power dispatching, integration of coordination and control, high efficiency of flow management and control, refinement of overall plan and the like are realized. According to the invention, by fusing the hadoop technology, the cloud computing technology and the fog computing technology, the intelligent power grid dispatching system firstly evaluates the execution time of the dispatching task when facing the task submitted by the user, judges whether the task belongs to an emergency task or a non-emergency task, and then dispatches the task to the corresponding platform according to the judgment result to execute the corresponding dispatching strategy, so that the optimal dispatching is realized, the resource distribution is more reasonable and flexible, the task dispatching efficiency is ensured, the resource utilization rate is improved, and the stability and the reliability of the power grid dispatching are ensured.
[ summary of the invention ]
In order to solve the above problems in the prior art, the invention provides a cloud and mist collaborative computing power grid scheduling method based on execution time evaluation, which comprises the following steps:
step S1: evaluating task execution time;
step S2: judging the type of a scheduling task;
step S3: and carrying out task scheduling based on the task scheduling optimization method of the improved genetic-ant colony algorithm.
Further, the step S1 includes: and effectively evaluating the execution time of the data task under the hadoop platform.
Furthermore, the distribution of data to be processed of the data tasks under the hadoop platform is obtained through task segmentation, and the description of each link of the scheduling tasks is completed by considering the network resources of the platform and the computing processing resources required by the data flow, so that the execution time of the scheduling tasks on the computing nodes is effectively evaluated, and the execution time of the data tasks of the hadoop platform can be obtained before actual scheduling execution.
Further, the hadoop platform comprises one or more task scheduling platforms.
Further, the task segmentation specifically includes: and partitioning the tasks based on the distribution of the data to be processed of the data tasks to obtain a subtask set and the data dependency relationship of the subtask set.
Further, the effective evaluation of the execution time of the task specifically includes: and evaluating network resources and computing resources required by each subtask in the subtask set to obtain the execution time of each subtask.
Further, based on the data dependency relationship between each subtask in the subtask set, taking the available network resources and the available computing resources as constraint conditions, performing execution time optimization to obtain the task execution time.
Furthermore, after the execution time of the scheduling task is obtained, the types of the scheduling task need to be classified by using a Qtsu rule based on the time, if the Qtsu rule determines that the scheduling task belongs to an urgent task, the scheduling task is scheduled to a fog platform close to the user for processing, and if the Qtsu rule determines that the scheduling task is not an urgent task, the scheduling task is scheduled to a cloud platform for processing, so as to reduce delay.
Further, the priority of the tasks is considered in the process of judging the type of the scheduling tasks.
Further, the task type is adjusted based on the priority in a weighting mode.
The invention has the beneficial effects that: by fusing the hadoop technology, the cloud computing technology and the fog computing technology, the intelligent power grid dispatching system firstly evaluates the execution time of the dispatching task when facing the task submitted by the user, judges whether the task belongs to an emergency task or a non-emergency task, and dispatches the task to a corresponding platform according to the judgment result to execute a corresponding dispatching strategy, so that optimal dispatching is realized, the resource distribution is more reasonable and flexible, the task dispatching efficiency is ensured, the resource utilization rate is improved, and the stability and the reliability of power grid dispatching are guaranteed.
[ description of the drawings ]
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, and are not to be considered limiting of the invention, in which:
FIG. 1 is a schematic diagram of a cloud and mist collaborative computing power grid dispatching system based on execution time evaluation according to the invention;
FIG. 2 is a schematic diagram of a cloud and mist collaborative computing power grid scheduling method based on execution time evaluation according to the invention;
FIG. 3 is a schematic diagram of the task scheduling optimization method of the improved genetic-ant colony algorithm of the present invention.
[ detailed description ] embodiments
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, the cloud and mist collaborative computing power grid dispatching system based on execution time evaluation includes: the system comprises a task scheduling queue, a task scheduling module and a resource pool;
the task scheduling queue is used for queuing tasks submitted by a user; for example: queuing the tasks according to a first-come-first-enter strategy; the task scheduling queue is a task scheduling queue in an operating system or a task scheduling queue in a Hadoop platform;
the task scheduling module is used for performing task execution time evaluation, scheduling task type judgment and scheduling platform selection;
the resource pool manages resources required by the scheduling object through an idle resource queue and a running resource queue; resource recovery is carried out after the task is executed, and resource allocation is carried out before the task is executed; the resource pool executes the scheduling task by using the scheduling object as an execution unit of the task; the scheduling object is a virtual machine, and the virtual machine is used as a resource carrier, configured with resources and operated in different task scheduling platforms; sharing the resource pool among the dispatching platforms;
preferably: the scheduling platform has the same priority for the resources in the resource pool, and can equally use the resources in the resource pool; alternatively: scheduling resources in the resource pool with unequal use of the platform, wherein the resource pool has unequal use rights for network resources; the scheduling platform organizes resources in the resource pool in a virtual limiting mode; for example: the virtual ranges defined by different scheduling platforms are different from different network resources, so that the use rights are different, which is actually suitable for the use overhead related to the network resources; similar situations exist on computing resources as well as storage resources;
the task scheduling platform comprises a cloud computing platform and a fog computing platform;
as shown in fig. 2, a schematic diagram of a cloud and mist collaborative computing power grid scheduling method based on execution time evaluation is shown, and the method mainly includes several steps of task execution time evaluation, scheduling task type judgment, scheduling platform selection, corresponding scheduling policy execution and the like; after a user submits a scheduling task, placing the task in a task scheduling queue according to a task submitting strategy; sequentially acquiring a first task in a task scheduling queue, effectively evaluating the execution time of the acquired task, judging whether the type of the task executed at this time is an emergency type or a non-emergency type according to the Qtsu rule by taking the evaluation time as a basis, putting the task on a corresponding platform for processing according to different types, and finally completing task scheduling processing by utilizing an improved genetic-ant colony algorithm.
The task is placed in a task scheduling queue according to a task submission strategy, and the method specifically comprises the following steps: inserting the tasks into corresponding positions in the task scheduling queue according to the task priority, so that the priorities of the tasks in the task scheduling queue are sequentially reduced from the head of the queue to the tail of the queue; the priority of the task is assigned by an operating system and/or specified by the user; because the task priority specified by an operating system or a user often has certain subjectivity and poor real-time performance, the traditional mode of queuing tasks only by depending on a task scheduling queue has poor effect, the particularity of the power grid tasks cannot be considered, and the task scheduling queue can only roughly sequence the tasks; alternatively: the tasks are put into a scheduling task queue according to the achieved sequence, and the mode is a common operating system task submission strategy;
the method comprises the following specific steps:
step S1: evaluating task execution time; and effectively evaluating the execution time of the data task under the hadoop platform. The distribution of data to be processed of the data tasks under the hadoop platform is obtained through task segmentation, and meanwhile, the description of each link of the scheduling tasks is finished by considering the network resources of the platform and the computing processing resources required by the data flow, so that the execution time of the scheduling tasks on the computing nodes is effectively evaluated, and the execution time of the data tasks of the hadoop platform can be obtained before actual scheduling execution.
The hadoop platform comprises one or more task scheduling platforms;
the task segmentation specifically comprises the following steps: dividing tasks based on the distribution of data to be processed of the data tasks to obtain a subtask set and a data dependency relationship thereof;
the effective evaluation of the execution time of the task specifically comprises the following steps: evaluating network resources and computing resources (storage resources) required by each subtask in the subtask set to obtain the execution time of each subtask; performing execution time optimization to obtain task execution time based on data dependency relationship between each subtask in the subtask set and using available network resources and available computing resources (storage resources) as constraint conditions; because the execution time of the tasks is effectively evaluated before the scheduling tasks are executed, all task sets at the stage can be reasonably considered and scheduled globally according to the evaluated execution time of the tasks, so that the scheduling resources are more reasonably utilized, and the execution time of the tasks is shortened.
Step S2: judging the type of a scheduling task; after the execution time of the scheduling task is obtained, the type of the scheduling task needs to be classified by utilizing a Qtsu rule based on the time, if the Qtsu rule judges that the scheduling task belongs to an emergency task, the scheduling task is scheduled to a fog platform close to a user for processing, and if the Qtsu rule judges that the scheduling task is not an emergency task, the scheduling task is scheduled to a cloud platform for processing, so that delay is reduced, the time for processing the task is greatly shortened, and the satisfaction degree of the user is improved.
Preferably: the priority of the tasks is considered in the judgment process of the scheduling task type; for example: adjusting the task type based on the priority in a weighting mode;
step S3: carrying out task scheduling based on a task scheduling optimization method of an improved genetic-ant colony algorithm; in the task scheduling optimization method based on the improved genetic-ant colony algorithm, firstly, a feasible strategy of task scheduling is quickly searched by using the genetic algorithm, then, initial pheromone distribution is determined according to the feasible strategy, then, an optimal solution of task scheduling is found by using the ant colony algorithm, and task scheduling is carried out based on the optimal solution.
As shown in fig. 3, the task scheduling optimization method for improving the genetic-ant colony algorithm specifically includes the following steps:
step S31: initializing the genetic parameters;
specifically, the genetic algorithm adopted is based on an IGA genetic algorithm;
step S32: executing coding operation, initializing a population by using a uniform distribution strategy, and calculating the fitness and the average fitness of individuals;
step S33: selecting two individuals with strong adaptability by adopting an elite reservation strategy, and carrying out selection, crossing and variation according to relative rules;
step S34, adopting an elite replacement strategy to replace the individuals in the step S33 with the individuals with the lowest fitness in the new generation, and accelerating the evolution of the individuals;
step S35: after the steps are finished, convergence judgment is carried out, if the maximum evolution iteration number or the specified calculation precision is reached, the evolution is immediately stopped, the optimal solution is output, and otherwise, the step S32 is carried out to continue the evolution;
step S36: initializing an ant colony; the method specifically comprises the following steps: and calculating original pheromones, and initializing the pheromones by analyzing service requests of users and combining the dependency relationship and priority sequence among tasks.
Step S37: selecting a scheduling object; the method specifically comprises the following steps: selecting a scheduling object by comprehensively considering a plurality of factors such as dependency relationship, priority order and the like among tasks; the scheduling object is an execution object of the scheduling task, for example: a virtual machine;
step S38: and (5) updating the pheromone. The updating of the pheromone comprises updating of local pheromones of the virtual machine, and the waste of resources is avoided by releasing in time. After the task is executed, releasing resources and putting the released resources into an idle resource queue;
step S39: completing task scheduling; after all the tasks to be scheduled are completely distributed, recording the shortest completion time of the tasks, updating the global pheromone of the scheduling object, and returning the task execution result of the scheduling object;
the invention provides a cloud and mist collaborative computing power grid scheduling method based on execution time evaluation. Different tasks are placed on different computing platforms for task scheduling by utilizing the cloud and fog collaborative computing platform and adopting a Qtsu rule, so that the pressure of processing the tasks by a single cloud computing platform is relieved, and the task scheduling efficiency is effectively improved. The task processing time is shortened.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A cloud and mist collaborative computing power grid scheduling method based on execution time assessment is characterized by comprising the following steps:
step S1: evaluating task execution time;
step S2: judging the type of a scheduling task;
step S3: and carrying out task scheduling based on the task scheduling optimization method of the improved genetic-ant colony algorithm.
2. The method according to claim 1, wherein the step S1 includes: and effectively evaluating the execution time of the data task under the hadoop platform.
3. The method as claimed in claim 1, wherein the distribution of data to be processed of the data task under the hadoop platform is obtained by task segmentation, and the description of each link of the scheduling task is completed by considering the network resources of the platform and the calculation processing resources required by the data stream, so as to effectively evaluate the execution time of the scheduling task on the calculation node, so that the execution time of the data task of the hadoop platform can be obtained before the actual scheduling execution.
4. The method of claim 1, wherein the hadoop platform comprises one or more task scheduling platforms.
5. The method according to claim 1, wherein the task segmentation is specifically: and partitioning the tasks based on the distribution of the data to be processed of the data tasks to obtain a subtask set and the data dependency relationship of the subtask set.
6. The method according to claim 1, characterized in that said efficient evaluation of the execution time of the task is carried out by: and evaluating network resources and computing resources required by each subtask in the subtask set to obtain the execution time of each subtask.
7. The method of claim 1, wherein the task execution time is obtained by performing execution time optimization based on data dependency between each subtask in the set of subtasks and using available network resources and available computing resources as constraints.
8. The method of claim 1, wherein after obtaining the execution time of the scheduled task, the type of the scheduled task needs to be classified according to the time by using a Qtsu rule, if the Qtsu rule determines that the scheduled task belongs to an urgent task, the scheduled task is scheduled to a fog platform close to a user for processing, and if the Qtsu rule determines that the scheduled task is not an urgent task, the scheduled task is scheduled to a cloud platform for processing, so as to reduce latency.
9. A method according to claim 1, characterized in that the priority of the tasks is taken into account in the determination of the type of scheduled task.
10. The method of claim 1, wherein the task type is adjusted based on priority by way of weighting.
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