Job scheduling prediction processing method and device and electronic equipment

Abstract

The invention discloses a job scheduling prediction processing method and device and electronic equipment, and the method comprises the steps: determining a prediction plan of a to-be-executed job at least according to the submitted job plan information after obtaining the job plan information of the submitted to-be-executed job, and executing the prediction processing of the to-be-executed job according to the prediction plan, predicting resource index values and / or resource states of a system under the condition that different assumed job activities of jobs to be executed in different time periods in the future are assumed to be executed respectively, obtaining a prediction result, generating an available execution time window set of the jobs to be executed according to the prediction result, and each available execution time window in the set can be used for enabling the resource state of the system resource to meet the state condition in the operation activity process of the to-be-executed operation, so that a proper operation time window can be planned for the to-be-executed operation through the available execution time window set finally, and resource competition for other services in the execution process of the to-be-executed operation is reduced.

Description

technical field [0001] The present application belongs to the field of job scheduling and resource management, and in particular relates to a job scheduling prediction processing method, device and electronic equipment. Background technique [0002] In a cloud computing environment, job activities share resources from the infrastructure to the platform level with other cloud computing jobs, and job activities may cause already heavily loaded jobs to run slower, or even cause jobs to time out or fail. For example, the Hadoop platform generates a large number of disk reads and writes that even reach the load threshold during the execution of multi-jobs, which causes a great impact on platform storage and application experience. Therefore, it is necessary to make a job scheduling plan in advance, so as to reduce the job's resource competition for other businesses through job scheduling. Contents of the invention [0003] For this reason, the application discloses following t...

AI Technical Summary

Problems solved by technology

[0002] In a cloud computing environment, job activities share resources from the infrastructure to the platform level with other cloud computing jobs, and job activities may cause already heavily loaded jobs to run slower, or even cause jobs to time out or fail

For example, the Hadoop platform generates a large number of disk reads and writes that even reach the load threshold during the execution of multi-jobs, which results in a great impact on platform storage and application experience.

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