A method of license dynamic prediction and scheduling based on data statistics

Abstract

The invention discloses a License dynamic predicting and scheduling method based on data statistics. The method comprises the steps that (a) users apply for registration, (b) operation priority is divided according to parallel nuclear numbers, (c) queuing for jobs is carried out, (d) whether idle License resources are available or not is judged, (e) whether jobs exceeding tolerant thresholds exist or not is judged, (f) jobs are selected from a job queue, and (g) software License resources and hardware resources are allocated. A dynamic prediction configuration method is adopted by the step (a) and dynamic scheduling of the jobs is achieved through the steps from (b) to (g). According to the License dynamic predicting and scheduling method, the dynamic prediction of the License resource applications under a public computing center, especially under a high performance computing platform can be carried out, floating authorized points of commercial software License can be allocated scientifically and efficiently, reasonable combination and using of the floating license points are guaranteed, and computing service quality of the public computing center can be improved effectively.

Description

technical field [0001] The present invention relates to a method for dynamic forecasting and scheduling of licenses based on data statistics, more specifically, to a method for dynamic forecasting and scheduling of licenses based on data statistics that is more reasonable and scientific for the sorting of waiting jobs and resource allocation . Background technique [0002] In many industries in the field of engineering and scientific computing, users need to use a large number of expensive commercial software for large-scale design and analysis. Spending a lot of money to buy each kind of software only for your own use will undoubtedly cost a lot of money. Statistics show that these users do not use most software very frequently. [0003] In recent years, with the development of high-performance computing, campuses, enterprises, provinces, and even public computing centers across the country have sprung up. More and more enterprises, universities, and scientific research ...

AI Technical Summary

Problems solved by technology

The license management mechanism of FlexLM can only realize the floating authorization of licenses in the local area network. It lacks the dynamic prediction and allocation method of software licenses, and cannot scientifically and effectively allocate the license resource applications of user operation groups. Therefore, public computing centers cannot rely on this mechanism alone. Optimal scheduling of jobs submitted by users

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