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Job scheduling method and apparatus

A job scheduling and job technology, which is applied in multi-programming devices, program startup/switching, program control design, etc., can solve the problems of starvation, large jobs cannot be scheduled for processing, etc., to shorten the response time, prevent starvation, The effect of improving the scheduling efficiency

Inactive Publication Date: 2017-07-25
XIAN ZHONGXING NEW SOFTWARE
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, it also needs to be considered that in practical applications, the small job priority strategy may cause the "starvation phenomenon" of large jobs. For example, in the case of large jobs, a large number of small jobs are continuously submitted to the MapReduce system. Jobs may cause large jobs to be unable to be scheduled and processed, resulting in "starvation phenomenon"

Method used

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  • Job scheduling method and apparatus
  • Job scheduling method and apparatus

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Embodiment 1

[0027] see figure 1 , which shows a job scheduling method provided by an embodiment of the present invention, the method may include:

[0028] S101: When there are idle resources, calculate the priority of each job in the job queue according to the size factor of each job in the job queue and the waiting factor of each job in the job queue, and put the job queue Jobs in are sorted according to the stated priority;

[0029] It should be noted that the priority of a job is dynamically calculated, and whenever there is an idle resource, the priority of each job in the job queue needs to be recalculated. During specific implementation, the job queue can be sorted according to the order of priority from high to low.

[0030]In the specific implementation process, when there are idle resources, all users are arranged into user queues according to fairness, and the job queues under the user name are scheduled in turn according to the order of the user queues from the head of the qu...

Embodiment 2

[0131] Based on the same technical idea of ​​the above-mentioned embodiment, see Figure 5 , which shows a device 50 provided by an embodiment of the present invention, the device 50 includes: a computing unit 501, a sorting unit 502, and a processing unit 503; wherein,

[0132] The calculation unit 501 is configured to calculate the priority of each job in the job queue according to the size factor of each job in the job queue and the waiting factor of each job in the job queue when there are idle resources;

[0133] The sorting unit 502 is configured to sort the jobs in the job queue according to the priority calculated by the computing unit 501;

[0134] The processing unit 503 is configured to schedule and process the jobs in the job queue according to the order of the sorted job queue.

[0135] In the above solution, the calculation unit 501 is specifically configured to calculate the size factor of each job in the job queue according to the task processing status of eac...

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Abstract

An embodiment of the invention discloses a job scheduling method. The method comprises the steps of calculating a priority of each job in a job queue according to a size factor of each job in the job queue and a waiting factor of each job in the job queue when idle resources occur, and sorting the jobs in the job queue according to the priorities; and according to a sequence of the sorted job queue, performing scheduling processing on the jobs in the job queue. Therefore, the response time of small jobs can be shortened, the ''hunger phenomenon'' of big jobs can be prevented, and the purpose of improving job scheduling efficiency is achieved.

Description

technical field [0001] The invention relates to job scheduling technology, in particular to a job scheduling method and device. Background technique [0002] MapReduce is currently a widely used distributed system processing framework for processing large-scale data. The scheduler is a core component of MapReduce and has a critical impact on the performance of the entire system. Currently, there are three common schedulers: fair scheduler and FIFO scheduler. [0003] As things stand, small jobs dominate the load. For example, in Facebook and Bing applications, jobs with less than 10 tasks accounted for 82% and 61% of the total jobs respectively, while in Google, the average execution time of MapReduce jobs was 395 seconds, and in Yahoo! In the production cluster of , more than 80% of the jobs completed in less than ten minutes. The large increase in small jobs is because small jobs such as random queries are increasingly running in MapReduce clusters, and latency is a cr...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F9/48
CPCG06F9/48G06F9/4881G06F2209/484
Inventor 郑鹏飞
Owner XIAN ZHONGXING NEW SOFTWARE
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