CN112231081B - PSO-AHP-based monotonic rate resource scheduling method and system in cloud environment - Google Patents
PSO-AHP-based monotonic rate resource scheduling method and system in cloud environment Download PDFInfo
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Abstract
The invention belongs to the field of resource scheduling of service robots, and provides a method and a system for scheduling monotonic rate resources based on PSO-AHP in a cloud environment. The PSO-AHP-based monotonic rate resource scheduling method in the cloud environment comprises the steps of obtaining a task period, a task value and a task running time of a scheduling cloud service, and constructing a comprehensive priority level analysis model and a corresponding judgment matrix; determining the weight of each factor in a criterion layer to an expert value of a scheme layer based on a PSO algorithm by taking the minimum consistency error of the judgment matrix as an optimization objective function, and calculating the comprehensive task priority of the scheduling cloud service; dividing the tasks of the scheduling cloud service into a high-priority task set and a low-priority task set according to the comprehensive priorities of the tasks; wherein the set of high priority tasks is scheduled preferentially. The method and the system reduce the total number of times of preemption of system tasks, can be well suitable for a cloud service platform, ensure the quality of cloud service and meet the QoS target constraint requirement of a user scheduling task.
Description
Technical Field
The invention belongs to the field of resource scheduling of service robots, and particularly relates to a PSO-AHP-based monotonic rate resource scheduling method and system in a cloud environment.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
In a complex cloud environment, how to efficiently schedule deployed services is a common NP-hard problem. The cloud computing system manages a large amount of virtualized resources, a resource scheduling method is a key component of the cloud computing system, and a resource scheduling process is shown in fig. 1. The method has the advantages that massive services and resource scheduling under the cloud service platform are researched, real-time management and efficient scheduling are carried out on a large amount of user services, the quality of the user services is improved on the premise that efficient operation of the cloud service platform is guaranteed, cost of a cloud service provider is reduced, and the method has very important theoretical value and practical significance. The execution efficiency of the task scheduling strategy in the cloud computing environment plays a crucial role in the cloud service quality, and the monotonic rate scheduling algorithm is a widely used static priority scheduling algorithm and has unique superiority in scheduling periodic tasks. However, the inventor finds that the traditional scheduling algorithm cannot guarantee the efficient operation of the cloud service platform, frequent preemption of tasks with different priorities causes a great deal of waste of system resources, and high-quality service cannot be provided for users. Therefore, the research on the improved RMS algorithm on the cloud service platform has high commercial value and practical significance.
Disclosure of Invention
In order to solve at least one technical problem in the background art, the invention provides a PSO-AHP-based monotonic rate resource scheduling method and system in a cloud environment, which effectively reduce task preemption times, reduce system resource waste, efficiently schedule services deployed in a complex cloud end, can be well suitable for a cloud service platform, ensure the quality of cloud services and meet the QoS target constraint requirement of a user scheduling task.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention provides a PSO-AHP-based monotonic rate resource scheduling method in a cloud environment.
A monotonic rate resource scheduling method based on PSO-AHP in a cloud environment comprises the following steps:
acquiring a task period, a task value and a task running time of a scheduling cloud service, and constructing a comprehensive priority level analysis model and a corresponding judgment matrix; the target layer of the comprehensive priority level hierarchical analysis model is used for obtaining task comprehensive priority levels, the criterion layer at least comprises three factors of a task period, a task value and task running time, and the scheme layer randomly obtains expert values of set groups;
determining the weight of each factor in a criterion layer to an expert value of a scheme layer based on a PSO algorithm by taking the minimum consistency error of the judgment matrix as an optimization objective function, and calculating the comprehensive task priority of the scheduling cloud service;
dividing the tasks of the scheduling cloud service into a high-priority task set and a low-priority task set according to the comprehensive priorities of the tasks; wherein the set of high priority tasks is scheduled preferentially.
The invention provides a PSO-AHP-based monotonic rate resource scheduling system in a cloud environment.
A PSO-AHP based monotonic rate resource scheduling system in a cloud environment comprises:
the comprehensive priority level analysis module is used for acquiring the task period, the task value and the task running time of the scheduling cloud service, and constructing a comprehensive priority level analysis model and a corresponding judgment matrix; the target layer of the comprehensive priority level hierarchical analysis model is used for obtaining task comprehensive priority levels, the criterion layer at least comprises three factors of a task period, a task value and task running time, and the scheme layer randomly obtains expert values of set groups;
the task comprehensive priority computing module is used for determining the weight of each factor in the criterion layer to the expert value of the scheme layer based on a PSO algorithm by taking the minimum consistency error of the judgment matrix as an optimization objective function, and computing the task comprehensive priority of the scheduling cloud service;
the task priority dividing module is used for dividing the tasks of the scheduling cloud service into a high-priority task set and a low-priority task set according to the comprehensive priorities of the tasks; wherein the set of high priority tasks are scheduled preferentially.
A third aspect of the invention provides a computer-readable storage medium.
A computer-readable storage medium, on which a computer program is stored, which when executed by a processor implements the steps in the PSO-AHP-based monotonic rate resource scheduling method in a cloud environment as described above.
A fourth aspect of the invention provides a computer apparatus.
A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to implement the steps of the PSO-AHP-based monotonic rate resource scheduling method in a cloud environment as described above.
Compared with the prior art, the invention has the beneficial effects that:
the invention not only introduces a PSO-AHP model to determine the influence of various factors on the comprehensive priority of the tasks, but also adds a task scheduling set to reduce the total times of the system tasks to be preempted, so that the scheduling in an RMS mode can be better transferred and scheduled, the invention can be well suitable for a cloud service platform, the quality of the cloud service is ensured, and the QoS target constraint requirement of the user scheduling tasks is met.
Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are included to illustrate an exemplary embodiment of the invention and not to limit the invention.
FIG. 1 is a cloud service platform task scheduling model according to an embodiment of the invention;
FIG. 2 is a schematic diagram of a method of improving monotonic rates;
FIG. 3 is a diagram of an AHP based integrated priority hierarchy analysis model;
FIG. 4 is a flow chart of a proposed PSO-AHP model in accordance with an embodiment of the present invention;
FIG. 5 is a diagram of task end completion times for an embodiment of the present invention;
FIG. 6 is a graph of task completion metrics for an embodiment of the present invention;
FIG. 7 is a task completion diagram of an embodiment of the present invention;
FIG. 8 is a graph of task loss for an embodiment of the present invention;
fig. 9 is a diagram of the number of times of task preemption, according to an embodiment of the present invention.
Detailed Description
The invention is further described with reference to the following figures and examples.
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
Interpretation of terms:
PARMS: refer to the abbreviation of monotonic rate resource scheduling method of PSO-AHP.
PSO: is an English abbreviation of Particle Swarm Optimization algorithm (Particle Swarm Optimization), is a random Optimization technology based on the population, is proposed by Eberhart and Kennedy in 1995, and generally depends on a model of bird foraging of the population to find an optimal value.
AHP: analytic Hierarchy Process (AHP) was proposed by american operational scientists, university of pittsburgh, professor t.l.saaty in the early 70's 20 th century, and AHP is a simple, flexible and practical multi-criteria decision-making method for quantitative analysis of qualitative problems. The method is characterized in that various factors in the complex problem are classified into interconnected ordered levels to be organized, expert opinions and objective judgment results of an analyst are directly and effectively combined according to a certain objective and realistic subjective judgment structure (mainly pairwise comparison), and the importance of pairwise comparison of elements of one level is quantitatively described.
Example one
The embodiment provides a monotonic rate resource scheduling method based on PSO-AHP in a cloud environment, which comprises the following steps:
step 1: acquiring a task period, a task value and a task running time of a scheduling cloud service, and constructing a comprehensive priority level analysis model and a corresponding judgment matrix; the target layer of the comprehensive priority level hierarchical analysis model is used for obtaining task comprehensive priority levels, the criterion layer at least comprises three factors of a task period, a task value and task running time, and the scheme layer randomly obtains expert values of set groups.
In the implementationAnd determining the influence of factors such as task period, task value and task running time on the comprehensive priority of the tasks. The task priority is proportional to the task value V and the task period T i In inverse proportion to task running time E i In inverse proportion. To quantitatively determine the expression of task priority, a task period is defined: total period/each subtask period; operating time: total runtime/subtask runtime.
From this, the expression of the comprehensive priority of each of the n tasks is given as:
wherein, K v 、K p And K t Respectively representing the weight of the task value, the task period and the task running time. Based on priority P i The task scheduling strategy not only ensures that high-value tasks are scheduled preferentially, but also effectively increases the completion number of tasks with lower processor utilization rate or reduces the deadline of the tasks, thereby improving the total value and the total task volume of the tasks.
For the target layer of the comprehensive priority level hierarchical analysis model to obtain the comprehensive priority of the task, the K is solved v 、K p And K t A value of (d); the criterion layer comprises three factors which influence the comprehensive priority of the tasks, namely the task period, the task value and the task running time, and the scheme layer randomly obtains five groups of expert values, as shown in figure 3.
And 2, step: and determining the weight of each factor in the criterion layer to the expert value of the scheme layer based on a PSO algorithm by taking the minimum consistency error of the judgment matrix as an optimization objective function, and calculating the comprehensive task priority of the scheduling cloud service.
Specifically, assume that the weight value of each factor of the criterion layer is w s And s is 1-m, if the matrix is judged to satisfy:
it is considered to have complete consistency. The optimization objective of the algorithm thus derived is:
wherein, a ij A jth element representing an ith row in the decision matrix; m represents the number of factors in a criterion layer in the surface analytic hierarchy process. a is is The s element representing the ith row in the decision matrix; CI' represents the consistency error of the judgment matrix, and the smaller the error value is, the higher the consistency of the judgment matrix is, and the more easily the judgment matrix is accepted; when the error is 0, it indicates that the judgment matrices have complete consistency. The smaller the value of CI ', the higher the degree of consistency of the judgment matrix, and when the value of CI' is 0, the judgment matrix has complete consistency. Calculating and optimizing the weight values of all factors of the criterion layer to be generalized to a target optimization problem, wherein an optimized target function is as follows:
the constraints of the objective function are:
each particle in the model of the PSO-AHP represents a set of weight values, and the algorithm ends the iteration if the maximum number of iterations is reached or the global optimal position meets the minimum limit, as shown in fig. 4.
And 3, step 3: dividing the tasks of the scheduling cloud service into a high-priority task set and a low-priority task set according to the comprehensive priorities of the tasks; where a set of high priority tasks is scheduled first, as shown in figure 2.
In specific implementation, all tasks are sorted in a descending order according to the obtained comprehensive priority, the tasks with the first two bits of the comprehensive priority are preferentially distributed to a set with a higher priority, and the tasks in the set are not allowed to be preempted during processing until the tasks are finished. The remaining tasks are divided into another set that is scheduled in the same way as the classical RMS algorithm.
In the embodiment, the implementation process of the monotonic rate resource scheduling method based on the PSO-AHP is to determine the comprehensive priority of the tasks through the PSO-AHP model, and divide the tasks according to the obtained priority. And dividing two phases to schedule the tasks, wherein the high-priority set tasks are scheduled preferentially. The tasks with the first two priorities are divided into a high-priority task set, the tasks are scheduled in a static non-preemptive RMS mode, the other tasks are divided into a low-priority task set, and the tasks are scheduled in a dynamic preemptive RMS mode.
The PSO-AHP-based monotonic rate resource scheduling method provided by the embodiment aims to reduce the number of task preemption and reduce the extra resource overhead and waste of the system caused by task context switching; the number of times of preemption can be reduced, which shows that the task priority obtained by the method is effective and reasonable, the scheduling effect of the algorithm is better, and the service quality obtained by the user is higher.
Simulation verification:
performing comparison simulation experiments of a common scheduling algorithm in a plurality of different scenes, and measuring the quality of a scheduling result by using a plurality of evaluation indexes, such as: the loss rate of the tasks, the completion amount of the tasks, the total value of the tasks and the like. The consumption behavior of the CPU resource by task preemption is added to the monotonic rate scheduling algorithm, and the time consumed by switching in and switching out a single task is about 4 ms. Fig. 5 and 6 are graphs comparing the final completion time and the total value amount of completion of various algorithms at different task amounts, fig. 7 reflects the task completion degree of the algorithms, and fig. 8 shows the change of the task loss amount. In fig. 9, PARMS refers to an abbreviation of monotonic rate resource scheduling method of PSO-AHP. As can be seen from fig. 9, the total number of times of task preemption is greatly reduced by the PARMS algorithm, which indicates that the task priority obtained by the method is more reasonable, the scheduling performance is better, the service quality is higher, and the user requirements are met.
Example two
The embodiment provides a monotonic rate resource scheduling system based on PSO-AHP in cloud environment, which comprises:
the comprehensive priority level analysis module is used for acquiring the task period, the task value and the task running time of the scheduling cloud service, and constructing a comprehensive priority level analysis model and a corresponding judgment matrix; the target layer of the comprehensive priority level hierarchical analysis model is used for obtaining task comprehensive priority levels, the criterion layer at least comprises three factors of a task period, a task value and task running time, and the scheme layer randomly obtains expert values of set groups;
the task comprehensive priority computing module is used for determining the weight of each factor in the criterion layer to the expert value of the scheme layer based on a PSO algorithm by taking the minimum consistency error of the judgment matrix as an optimization objective function, and computing the task comprehensive priority of the scheduling cloud service;
the task priority dividing module is used for dividing the tasks of the scheduling cloud service into a high-priority task set and a low-priority task set according to the comprehensive priorities of the tasks; wherein the set of high priority tasks is scheduled preferentially.
The specific implementation process of each module in the monotonic rate resource scheduling system based on PSO-AHP in the cloud environment of this embodiment is the same as the specific implementation process of each step in the monotonic rate resource scheduling method based on PSO-AHP in the cloud environment of the first embodiment, and will not be described here again.
EXAMPLE III
The present embodiment provides a computer-readable storage medium, on which a computer program is stored, and when executed by a processor, the computer program implements the steps in the method for scheduling monotonic rate resource based on PSO-AHP in cloud environment as described in the first embodiment.
Example four
The embodiment provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor executes the program to implement the steps in the PSO-AHP-based monotonic rate resource scheduling method in the cloud environment as described in the first embodiment.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above may be implemented by a computer program, which may be stored in a computer readable storage medium and executed by a computer to implement the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A monotonic rate resource scheduling method based on PSO-AHP in a cloud environment is characterized by comprising the following steps:
acquiring a task period, a task value and a task running time of a scheduling cloud service, and constructing a comprehensive priority level analysis model and a corresponding judgment matrix; the target layer of the comprehensive priority level analysis model is used for solving weighted values of task values, task periods and task running time in the comprehensive task priority level expression, the criterion layer at least comprises three factors of the task periods, the task values and the task running time, and the scheme layer randomly obtains expert values of set groups;
determining the weight of each factor in a criterion layer to an expert value of a scheme layer based on a PSO algorithm by taking the minimum consistency error of the judgment matrix as an optimization objective function, and calculating the comprehensive task priority of the scheduling cloud service;
specifically, assume that the weight value of each factor of the criterion layer is w s And s is 1: m, if the matrix is judged to satisfy:
it is considered to have complete consistency; the optimization objective of the algorithm thus derived is:
wherein, a ij A jth element representing an ith row in the decision matrix; m represents the number of factors in a criterion layer in the surface analytic hierarchy process; a is is The s element representing the ith row in the decision matrix; CI' represents the consistency error of the judgment matrix, and the smaller the error value is, the higher the consistency of the judgment matrix is, and the more easily the judgment matrix is accepted; when the error is 0, the judgment matrix has complete consistency; the smaller the value of CI ', the higher the consistency degree of the judgment matrix is, and when the value of CI' is 0, the judgment matrix has complete consistency; calculating and optimizing the weight values of all factors of the criterion layer to be generalized to a target optimization problem, wherein an optimized target function is as follows:
the constraints of the objective function are:
each particle in the PSO-AHP model represents a group of weight values, and when the algorithm reaches the maximum iteration times or the global optimal position meets the minimum limit, the iteration is ended;
dividing the tasks of the scheduling cloud service into a high-priority task set and a low-priority task set according to the comprehensive priorities of the tasks; wherein the high priority task set is scheduled preferentially; determining the comprehensive priority of the tasks through a PSO-AHP model, and dividing the tasks according to the obtained priority; dividing two stages to schedule the tasks, and scheduling the high-priority set tasks in a priority mode; the tasks with the first two priorities are divided into a high-priority task set, the tasks are scheduled in a static non-preemptive RMS mode, the other tasks are divided into a low-priority task set, and the tasks are scheduled in a dynamic preemptive RMS mode.
2. The PSO-AHP-based monotonic rate resource scheduling method in the cloud environment as claimed in claim 1, wherein all tasks are sorted in a descending order according to the comprehensive priority of the tasks, the tasks with the preset number of bits before the priority are divided into a high priority task set, and the rest tasks are divided into a low priority task set.
3. The PSO-AHP-based monotonic rate resource scheduling method in the cloud environment as recited in claim 1, wherein tasks in the high priority task set are scheduled in a static non-preemptive RMS manner.
4. The PSO-AHP-based monotonic rate resource scheduling method in the cloud environment as recited in claim 1, wherein tasks in a low priority task set are scheduled in a dynamic preemptive RMS manner.
5. The PSO-AHP-based monotonic rate resource scheduling method in the cloud environment as recited in claim 1, wherein a task comprehensive priority for scheduling the cloud service is proportional to a task value, inversely proportional to a task period, and inversely proportional to a task running time.
6. The PSO-AHP-based monotonic rate resource scheduling method in the cloud environment as claimed in claim 1 or 5, wherein a task period is a total task period/each subtask period; the task running time is the total running time of the task/the running time of each subtask.
7. The PSO-AHP-based monotonic rate resource scheduling method in the cloud environment as recited in claim 1, wherein in the process of determining the weight of each factor in the criteria layer to the solution layer expert value based on the PSO algorithm, each particle represents a group of weight values, when the PSO algorithm reaches the maximum iteration number or the global optimal position meets the minimum limit, the iteration is ended, and the weight of each factor in the criteria layer to the solution layer expert value is output.
8. A PSO-AHP based monotonic rate resource scheduling system in a cloud environment is characterized by comprising:
the comprehensive priority level analysis module is used for acquiring the task period, the task value and the task running time of the scheduling cloud service, and constructing a comprehensive priority level analysis model and a corresponding judgment matrix; the target layer of the comprehensive priority level analysis model is used for solving weighted values of task values, task periods and task running time in the comprehensive task priority level expression, the criterion layer at least comprises three factors of the task periods, the task values and the task running time, and the scheme layer randomly obtains expert values of set groups;
the task comprehensive priority computing module is used for determining the weight of each factor in the criterion layer to the expert value of the scheme layer based on a PSO algorithm by taking the minimum consistency error of the judgment matrix as an optimization objective function, and computing the task comprehensive priority of the scheduling cloud service; specifically, assume that the weight value of each factor of the criterion layer is w s And s is 1: m, if the matrix is judged to satisfy:
it is considered to have complete consistency; the optimization objective of the algorithm thus derived is:
wherein, a ij A jth element representing an ith row in the decision matrix; m represents the number of factors in a criterion layer in the surface analytic hierarchy process; a is is The s element representing the ith row in the decision matrix; CI' represents the consistency error of the judgment matrix, and the smaller the error value is, the higher the consistency of the judgment matrix is, and the more easily the judgment matrix is accepted; when the error is 0, it means that the judgment matrix is completeFull consistency; the smaller the value of CI ', the higher the consistency degree of the judgment matrix is, and when the value of CI' is 0, the judgment matrix has complete consistency; calculating and optimizing the weight values of all factors of the criterion layer to be generalized to a target optimization problem, wherein an optimized target function is as follows:
the constraints of the objective function are:
each particle in the PSO-AHP model represents a group of weight values, and when the algorithm reaches the maximum iteration times or the global optimal position meets the minimum limit, the iteration is ended;
the task priority dividing module is used for dividing the tasks of the scheduling cloud service into a high-priority task set and a low-priority task set according to the comprehensive priorities of the tasks; wherein the high priority task set is scheduled preferentially; determining the comprehensive priority of the tasks through a PSO-AHP model, and dividing the tasks according to the obtained priority; dividing two stages to schedule the tasks, and scheduling the high-priority set tasks in a priority mode; the tasks with the first two priorities are divided into a high-priority task set, the tasks are scheduled in a static non-preemptive RMS mode, the other tasks are divided into a low-priority task set, and the tasks are scheduled in a dynamic preemptive RMS mode.
9. A computer-readable storage medium, on which a computer program is stored, wherein the program, when executed by a processor, implements the steps in the PSO-AHP-based monotonic rate resource scheduling method in a cloud environment of any of claims 1-7.
10. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the steps in the PSO-AHP based monotonic rate resource scheduling method in a cloud environment of any one of claims 1-7.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8776074B1 (en) * | 2010-01-22 | 2014-07-08 | Lockheed Martin Corporation | Methods, devices and systems for coordinating and optimizing resources |
| CN106610866A (en) * | 2016-06-17 | 2017-05-03 | 四川用联信息技术有限公司 | Service value constrained task scheduling algorithm in cloud storage environment |
| CN109543875A (en) * | 2018-10-16 | 2019-03-29 | 华南理工大学 | Cloud workflow schedule method based on collected discrete particle swarm optimization |
| CN110413389A (en) * | 2019-07-24 | 2019-11-05 | 浙江工业大学 | A kind of task schedule optimization method under the unbalanced Spark environment of resource |
-
2020
- 2020-10-14 CN CN202011099006.0A patent/CN112231081B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8776074B1 (en) * | 2010-01-22 | 2014-07-08 | Lockheed Martin Corporation | Methods, devices and systems for coordinating and optimizing resources |
| CN106610866A (en) * | 2016-06-17 | 2017-05-03 | 四川用联信息技术有限公司 | Service value constrained task scheduling algorithm in cloud storage environment |
| CN109543875A (en) * | 2018-10-16 | 2019-03-29 | 华南理工大学 | Cloud workflow schedule method based on collected discrete particle swarm optimization |
| CN110413389A (en) * | 2019-07-24 | 2019-11-05 | 浙江工业大学 | A kind of task schedule optimization method under the unbalanced Spark environment of resource |
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