Cloud workflow task execution time prediction method based on limit gradient improvement

Abstract

The invention relates to a cloud workflow task execution time prediction method based on limit gradient improvement, and belongs to the technical field of cloud computing. According to the method, influence factors of task execution time are classified from three levels of workflow task composition, resources on which task operation depends and a physical execution environment of the resources, and comprehensive modeling of the influence factors of the task execution time is achieved. Secondly, aiming at the condition that the sample data set has a data missing value, the data set with the missing value is complemented by adopting a machine learning method; and finally, by means of the multi-type data processing capability of the extreme gradient lifting algorithm, the parameter design isrelatively simple, the calculated amount is small, the advantages of a serial learner and a parallel learner are combined, and a cloud workflow task execution time prediction model is trained by adopting the extreme gradient lifting algorithm. Compared with an existing prediction model, limitation on the sample data type is reduced, prediction errors are reduced, and the generalization ability ofthe model is further improved.

Description

technical field [0001] The invention relates to a method for predicting the execution time of a cloud workflow task, and belongs to the technical field of cloud computing. Background technique [0002] With the maturity and wide application of cloud computing technology, especially the pay-per-use model of cloud computing and the elastic and on-demand expansion of cloud resources, users can obtain anytime, anywhere and at low cost without investing in hardware equipment and software resources. Any required computing resource service, therefore, more and more scientific workers use the cloud computing environment to support and execute their own complex scientific computing processes, that is, scientific workflow or cloud workflow. [0003] The first step in cloud workflow execution is the scheduling of tasks to virtual machine resources, that is, from the virtual resource pool, match the most suitable virtual machine resources for each cloud workflow task, aiming to meet the...

AI Technical Summary

Problems solved by technology

[0007] The present invention aims to solve the problem of accurate estimation and prediction of cloud workflow task execution time, and proposes a cloud workflow task execution time prediction method based on extreme gradient boosting

Secondly, in the case of missing values ​​in the sample data sets, machine learning methods such as random forest models are used to complete the data sets with missing values, which solves the inherent missing values ​​of traditional methods such as average filling and K-nearest neighbor filling. Loss of variability, over-dependence on observations, and poor robustness

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