Cloud platform task maximum resource utilization rate prediction method

Abstract

The invention discloses a method for predicting the maximum resource utilization rate of a task in a cloud platform in a future period of time. The method includes the steps: based on historical resource usage information of multiple tasks of a cloud platform, extracting preliminary task resource usage characteristics by analyzing resource usage conditions of cloud platform tasks; further extracting features by using a sparse self-coding model; then, clustering the tasks by using a K-medoids clustering method, and training each task category by using an improved random forest regression model;and finally, predicting the maximum resource utilization rate of a given task in a future period of time by using the trained model based on the historical information of the resource utilization rate of the task in the latest period of time. Meanwhile, according to the resource use features of the cloud platform task, the invention also designs a resource use prediction performance evaluation function, namely an improved average absolute percentage error, suitable for the task, and the evaluation function can more visually reflect the performance difference of different task resource use prediction methods.

Description

technical field [0001] The invention relates to the field of cloud computing, in particular to a method for predicting the maximum resource usage rate of a task in a cloud platform. Background technique [0002] Although cloud computing provides a convenient and flexible resource management method, the resource utilization rate of most existing cloud platforms is still relatively low. For example, the total CPU utilization rate of thousands of servers on the Twitter cloud platform within a month is always lower than 20%. However, the reserved resources reached 80% of the total resources; the average CPU usage of Google Cloud Platform fluctuated between 10% and 45%. Predicting the resource usage of tasks is one of the important means to improve the resource usage of the cloud platform. However, the dynamics, uncertainty and abruptness of cloud platform task resource usage make forecasting difficult. Most of the existing related studies are based on simulation data, and the ...

AI Technical Summary

Problems solved by technology

However, the dynamic, uncertain and abrupt nature of cloud platform task resource usage makes forecasting difficult.

Most of the existing related research is based on simulation data, and the final experimental results are not convincing

For the real cloud platform data set, the use of backpropagation neural network model and random forest regression model (Breiman L.Randomforests[J].Machine learning,2001,45(1):5-32) has been proposed to carry out task load However, because these models do not fully consider the characteristics of cloud platform task resource usage, the performance of prediction needs to be further improved

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