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A method for scheduling and configuring resources of associated task flows in the cloud

A resource scheduling and configuration method technology, applied in the field of cloud computing services, can solve problems affecting task scheduling schemes, unstable execution time, and failure to consider workflow scheduling processes, etc.

Active Publication Date: 2019-03-29
中国人民解放军陆军防化学院
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

At the same time, adopting the method of backup means generating additional costs. The increase in the number of backups will lead to an increase in the stability of task execution and an increase in cost. It is necessary to find a resource optimization allocation method that can comprehensively consider service costs and service quality. Improve resource utilization efficiency
The method of using backup to solve the problem of unstable execution time has been mentioned in some works, but these studies have not considered the backup task selection strategy and the determination strategy of the number of backups
In addition, many existing research results have not considered the changes in the workflow scheduling process caused by the use of backup methods. When multiple tasks are executed in parallel, any task may become the main task, which will give workflow correlation Task determination brings uncertainty, which affects the selection of subsequent tasks and the generation of the entire scheduling plan

Method used

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  • A method for scheduling and configuring resources of associated task flows in the cloud
  • A method for scheduling and configuring resources of associated task flows in the cloud
  • A method for scheduling and configuring resources of associated task flows in the cloud

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Experimental program
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Embodiment 1

[0037] see figure 1 , which is a flow chart of a resource scheduling and configuration method for associated task flows in the cloud provided by this embodiment. A resource scheduling configuration method for associated task flows in the cloud, comprising the following steps:

[0038] S1: Determine the backup priority of each subtask in the associated task flow.

[0039] Optionally, the specific method for determining the backup priority is as follows:

[0040] S101: Assign different weights to subtask parameters, wherein the task parameters include the number of task successor tasks, the number of task predecessor tasks, the theoretical execution time of the task, and the path length from the task to the end task, and the end task is an associated task flow The last subtask executed in .

[0041] S102: Obtain the number of subsequent tasks of each subtask, the number of predecessor tasks, the theoretical execution time of the task, and the path length from the task to the ...

Embodiment 2

[0057] When multiple tasks are executed in parallel, any task may become the main task, which will bring uncertainty to the determination of the associated task flow of the workflow, thereby affecting the selection of subsequent tasks and the generation of the entire scheduling plan; in order to reduce The impact of the backup policy on the scheduling process of associated task flows, based on the above embodiments, this embodiment provides another method for resource scheduling and configuration of associated task flows in the cloud.

[0058] After the resource scheduling configuration of the associated task flow is completed as described in step S4, the following steps are also performed:

[0059] Judging whether there is a backup task that has been completed in the subtasks with multiple backups, if so, record the completed backup task as task Y, and determine that the performance of the computing resource where task Y is located is actually the best, and give up the executi...

Embodiment 3

[0066] Computing resources in the cloud are limited. In order to improve the utilization rate of the computing resources, based on the above embodiments, this embodiment provides another resource scheduling configuration method for associated task flows in the cloud.

[0067] After the resource scheduling configuration of the associated task flow is completed as described in step S4, the following steps are also performed:

[0068] When the computing resources are insufficient, obtain the execution progress of each backup task in the subtasks that are being executed and have multiple backups, and only keep the M backup tasks with the fastest execution progress to realize the release of computing resources. The determination method of M is as follows: :

[0069] Obtain the average value of the execution progress of each backup task;

[0070] Obtain the product of the average value and the number of backup tasks;

[0071] Rounding up the product yields M.

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Abstract

The invention provides a method for scheduling and configuring resources of associated task flows in a cloud. The method includes: according to the set weight of task parameters, determining the backup priority for each subtask in the associated task flow, and then reconstructing the associated task flow according to the preset backup model, looking for local critical paths in the reconstructed associated task flow model, assigning computing resources with the best performance to the sub-tasks with the highest backup priority, considering the relationship between the execution cost of associated task flow and computing resources, taking the execution time of associated task flow as the main optimization objective, the execution cost of backup strategy can be reduced as much as possible onthe basis of the shortest execution time of tasks.

Description

technical field [0001] The invention belongs to the field of cloud computing services, in particular to a method for resource scheduling and configuration of associated task flows in the cloud. Background technique [0002] Most of the existing commercial clouds use the billing model of charging users according to the service time, which requires users to have a more accurate estimate of the execution time of the submitted tasks, otherwise it will cause budget uncertainty, and then the The quality of the entire service model poses challenges. However, in most cases in reality, it is difficult for users to form a completely clear understanding of the calculation amount of the task, so it is difficult to accurately estimate the execution time of the task. On the other hand, due to environmental and technical reasons, the existing cloud computing environment is not stable in terms of computing performance and data transmission performance, and the computing performance of comp...

Claims

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Application Information

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IPC IPC(8): G06F9/50G06F11/14
CPCG06F9/5005G06F11/1464
Inventor 纪浩然孙玉萍武云鹏王健稳刘国宏李广峰马海峰
Owner 中国人民解放军陆军防化学院
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