A Scheduling Method for Algorithm Execution and Monitoring

Abstract

The invention relates to a scheduling method for algorithm execution and monitoring. The method includes: (1) This method divides the algorithms that need to be scheduled and executed into three categories, and gives the definitions of different types of algorithms; (2) Provides different types of algorithm scheduling and execution frameworks, and encapsulates the algorithms according to the different algorithm classifications. Different types of algorithms provide different scheduling methods, and monitor the status of the feedback algorithm execution process. (3) According to the definition of this method, divide the algorithms that need to be scheduled and execute, and set the algorithm through the corresponding algorithm scheduling configuration table in the database. Scheduling configuration, (4) This scheduling method performs algorithm scheduling execution and monitoring according to the algorithm settings in the database. According to different types of algorithms, a mutual exclusion guarantee mechanism is provided to ensure that the algorithm will not be repeatedly scheduled and executed when it is not completed. The method can simplify algorithm classification, improve algorithm scheduling and execution efficiency, and monitor algorithm execution.

Description

technical field [0001] The invention relates to the field of computer application technology and methods, in particular to a scheduling technical solution for algorithm execution and monitoring. Background technique [0002] Since the beginning of the new century, the development of science and the maturity of information technology have accelerated the development of various data-based analysis methods, which have better overcome the problems of modeling difficulties that commonly exist in complex process systems. However, due to the variety of data analysis algorithms and different usage scenarios, most of the execution of the algorithms is still controlled manually, and there is a lack of an automatic control and scheduling method. And there is a lack of means to monitor and record the overall implementation of the algorithm. Especially for the current big data processing and other scenarios, which involve a large number of algorithm execution scheduling and monitoring p...

AI Technical Summary

Problems solved by technology

However, due to the variety of data analysis algorithms and different usage scenarios, most of the execution of the algorithms is still controlled manually, and there is a lack of an automatic control scheduling method

And there is a lack of means to monitor and record the overall implementation of the algorithm

Especially for the current big data processing and other scenarios, which involve a large number of algorithm execution scheduling and monitoring problems, due to the variety of data and the large number and types of algorithms, relying on manual or semi-automatic methods to conduct data analysis and control scheduling of mining algorithms will affect Overall efficiency, and increased manpower consumption

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