Scheduling method, scheduler, storage medium and system

A scheduling method and scheduler technology, applied in the field of communications, can solve problems such as long job execution time, affecting cloud computing system performance, and low computing efficiency.
CN110297699AActive Publication Date: 2019-10-01HUAWEI TECH CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
HUAWEI TECH CO LTD
Publication Date
2019-10-01

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Abstract

The invention provides a scheduling method, a scheduler, a storage medium and a system, and belongs to the technical field of communication. The scheduling method comprises the steps: acquiring an incidence relation graph and data distribution information, wherein each node in the incidence relation graph refers to one piece of data, and each directed edge is used for representing an operation request for calculating according to the data referenced by a source node to obtain the data referenced by a target node; according to a preset node sorting strategy, sequentially positioning the operation requests corresponding to the traversed nodes to any server where the data indicated by the traversed nodes are located; and sequentially scheduling at least one job request represented by at leastone directed edge to the positioned server. For the scheduling method, the incidence relation graph is used for representing the incidence relation between the data and the corresponding relation between the operation requests and the data, and the data distribution information is used for representing the data distribution condition, so that cross-node data transmission can be reduced, the operation execution time of the whole operation request is shortened, and the calculation efficiency is improved, and the system performance is improved.
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Description

technical field

[0001] The present disclosure relates to the technical field of communication, and in particular to a scheduling method, a scheduler, a storage medium and a system. Background technique

[0002] With the growth of data scale and the development trend of data diversity, data analysis and processing technology is also constantly improving. At present, a variety of data analysis and processing architectures have emerged, such as Hadoop / MapReduce (distributed computing / mapping and merging) architecture , Spark, a big data processing framework based on memory computing, etc. Big data applications developed based on these data analysis and processing architectures can improve the efficiency of big data analysis and processing, and meet the real-time requirements of big data analysis and processing to a certain extent.

[0003] When implementing big data applications, it is necessary to build a computing cluster with a certain scale, and dynamically adjust the comp...

Claims

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